Excess Mortality And Vaccines In Europe (v2)

Author: Justin Garza

Date: See below

Description:
This notebook explores excess mortality across Europe, analyzing statistical trends and investigating potential causes through data visualization and interpretation.

Content Warning:
If you find discussions of death or its underlying factors distressing, please proceed with caution or consider whether this content is right for you.

from datetime import datetime
from IPython.display import display
from IPython.display import Markdown as MD
current_date = datetime.now().strftime('%Y-%m-%d')
version = datetime.now().strftime('%Y%m%d.%H%M')
display(MD(f"**Date:** {current_date}"))
display(MD(f"**version:** {version}"))

Date: 2025-04-06

version: 20250406.2149

Table of Contents

import os
import json5 as json
from IPython.display import display, HTML
from IPython.display import Markdown as MD

nb_path = os.path.join(os.getcwd(),'main.ipynb')

# Read the notebook
with open(nb_path, 'r', encoding='utf-8') as f:
    nb_data = json.load(f)

# Extract headers from markdown cells
outline = []
for cell in nb_data.get("cells", []):
    if cell["cell_type"] == "markdown":
        for line in cell["source"]:
            if line.startswith("#"):  # Markdown header
                level = line.count("#")  # Determine header level
                title = line.lstrip("#").strip()
                outline.append({
                        'text':title,
                        'level':level-1,
                        'link': f"index.html#{title.replace(' ','-')}"
                        })

for i in outline:
    display(HTML(f'<a href=\'{i["link"]}\'>{"#" * i["level"]} {i["text"]}</a>'))
    # print(f'<a href=\'{i["link"]}\'>{"#" * i["level"]}{i["text"]}</a>')

Excess Mortality And Vaccines In Europe (v2)

# Table of Contents

# Prerequisites

## Scientific Method

## Logical Fallacies

### About Appeal to Authority

#### And More ...

### About Post Hoc Ergo Propter Hoc (Correlation vs Causation)

# Observation

## News Articles & Headlines

### Ad Hominem Attacks

### Appeals to Authority

### Appeal to Emotions

## Denial of Aid

# Hypothesis

# below we will be

# Setup

# Helping Functions

# Import and Clean Data

## death data

### Getting the Data

### variables

## cause of death

### Getting the Data

### variables

## vaccine data

### Getting the Data

### variables

# combine all data

## variables

# Lets Visualize the Data

## Line Charts - normalized deaths

## HeatMaps of deaths by AgeGroups

### Post HeatMaps Notes

## Spearman and Kendall

## Spearman and Kendall ... continued.

## Other death x vaccine charts

## lets look at Deaths for each year 2015-2024 separately

## notes:

## Bad-Batch Theory

### There seems to be to be an unusual grouping/cluster

### Bad-Batch Theory (by Vibeke Manniche)

### my 2 cents

# Lets look at some of the Causes of Death

## Hypothesizing Adverse Reactions from an mRNA Vaccine

### Spearman and Kendall ... (again)

### Note:

# Lets see the Spearmans Rank over time

## Note:

# lets see these overtime

# notable CODs x Vaccines

## Post Axis Notes:

## here is the Scatter Chart of Malignant neoplasms (C00-C97) x Vaccine

misc_and_style

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Prerequisites

Scientific Method

The scientific method is a systematic approach to investigating natural phenomena, acquiring knowledge, and testing hypotheses. It consists of the following key steps:

1. Observation

   • Identify a problem or phenomenon that needs explanation.
   • Gather initial data through direct observation or research.

2. Hypothesis

   • Propose a testable and falsifiable explanation (a hypothesis).
   • Example: "If plants receive more sunlight, then they will grow taller."

3. Experimentation

   • Design and conduct controlled experiments to test the hypothesis.
   • Include independent and dependent variables, control groups, and repeatable procedures.

4. Conclusion

   • Determine whether the data supports or refutes the hypothesis.
   • Modify or refine the hypothesis if necessary.

5. Replication

   • Repeat experiments to verify results.
   • Publish findings for scrutiny by the scientific community.

   Rant!: Peer Review is Flawed

   Imagine a mechanic repairs my car and then writes a paper about it.
   Other mechanics review and approve the paper.
   But when I get my car back, it still won’t start.
   The mechanic insists, "But my paper was peer-reviewed!"
   No matter how many experts approved the paper, what really matters is whether the experiment—getting the car to run—actually worked.

The scientific method ensures objectivity, reliability, and accuracy in scientific inquiry. It is an iterative process, meaning that conclusions can lead to new questions and further investigations.

Logical Fallacies

Logical fallacies are errors in reasoning that weaken arguments. They can be categorized into formal (structural errors) and informal (content errors).

Type	Fallacy	Description
Formal	Affirming the Consequent	Assuming that if P → Q and Q is true, then P must be true.
	Denying the Antecedent	Assuming that if P → Q and P is false, then Q must be false.
	Non-Sequitur	The conclusion does not logically follow from the premises.
Informal – Relevance	Ad Hominem	Attacking the person instead of the argument.
	Straw Man	Misrepresenting an argument to make it easier to attack.
	Red Herring	Diverting attention with an irrelevant point.
	Appeal to Authority	Claiming something is true because an authority said so.
	Appeal to Emotion	Using emotions instead of logic to argue a point.
Informal – Causation & Presumption	Post Hoc Ergo Propter Hoc	Assuming that correlation implies causation.
	Slippery Slope	Claiming one action will lead to extreme consequences.
	False Dilemma	Presenting only two options when more exist.
	Begging the Question	Using circular reasoning.
	False Equivalence	Treating two things as equal when they are not.
	Hasty Generalization	Drawing a conclusion from insufficient evidence.
	No True Scotsman	Excluding counterexamples by redefining a group.

Logical fallacies can make arguments misleading or invalid. Identifying them helps improve critical thinking and debate skills.

About Appeal to Authority

DOCTORS USED TO PRESCRIBE CIGARETTES

And More ...

• Bloodletting
• Lobotomies
• Radium and Mercury Treatments
• Thalidomide for Morning Sickness
• Cocaine and Heroin as Medicine
• X-Ray Shoe Fitting
• Forceps and Twilight Sleep in Childbirth
• Tapeworm Diet Pills
• Electroshock Therapy (Overuse)

Therefore Doctors need to provide something more than just saying they are an authority on a subject.

About Post Hoc Ergo Propter Hoc (Correlation vs Causation)

• Flipping a switch and seeing the light turn on implies causation, not just correlation.

  • Suggesting mere correlation in this case would seem absurd.
  • without repetedly flipping the switch to test it ... we would still stay causation because:
    1. Mechanistic Understanding
       • We know how light switches work; they complete a circuit to turn the light on.
    2. Temporal Order
       • The switch is flipped first, and the light turns on immediately after, fitting the cause-effect pattern.
    3. Location Relevance
       • in this example, we are assuming the light came on in the same house where the light switch was (not a different building across town)
    4. Alternative Explanations
       • A purely correlational event would imply something external
         • (e.g., a power surge or someone else activating the light), which is far less probable.

• Later on we will talk about vaccines, and if they work ideally they should have an impact on deaths within a country.

  1. Mechanistic Understanding
     • we all know that vaccines save lives and are safe
       • from constantly being told that in schools and the medical system
  2. Temporal Order
     • vaccines doses are given out, then there should be less people dying.
  3. Location Relevance
     • we should see the location relevance (european countries) that took more vaccines vs less vaccines.
       • unforetunatly, we don't have perfect data so that a country that took zero vaccines did not exists within the dataset.
  4. Alternative Explanations
     • Plausible alternative explanations may exist, but they require evidence to support them.
       • Suggesting an alternative without supporting evidence is a red herring, distracting from the most logical conclusion.
     • Plausible alternatives should also be relavent to the scale we are refering to.
       • As a rule of thumb a small cause - small event, big cause - big event.

Observation

There were two sides when it comes to the vaccines

• The covid-19 vaccines were bad, and cause side effects (including death)
• The covid-19 vaccines were good and saved lives, and is safe.

News Articles & Headlines

The One side of this can easily be seen in the news headlines, using logical fallacies

Ad Hominem Attacks

• CDC Warns of 'Pandemic of the Unvaccinated'
• Covid: French uproar as Macron vows to 'piss off' unvaccinated
• Don Lemon Unloads on Unvaxxed: We Have to ‘Do Things For The Greater Good Of Society, Not For Idiots’
• People Who Skip Vaccinations 'Incredibly Selfish' Experts Say
• "If you're willing to walk among us unvaccinated, you are an enemy." - Gene Simmons, co-lead singer and co-founder of KISS
• plague rats
• selfish
• anti-science
• ignorant
• irresponsible.

Appeals to Authority

• Pope Francis urges people to get vaccinated against Covid-19
• Former Presidents Obama, Bush and Clinton volunteer to get coronavirus vaccine publicly to prove it’s safe
• FDA Approves First COVID-19 Vaccine

Appeal to Emotions

• Getting Vaccinated to Help Protect Yourself, Your Family and Your Community
• COVID-19 Vaccines Protect the Family, Too
• Concern about loved ones might motivate people to mask up, get vaccine

Denial of Aid

One side of this topic also had the power to denie aid and services.

• D.J. Ferguson
  • Service Denied: Heart transplant at Brigham and Women’s Hospital (2022).
  • Reason: Refused COVID-19 vaccine, a hospital requirement.
• Leilani Lutali
  • Service Denied: Kidney transplant at UCHealth (2021).
  • Reason: Opposed vaccine due to religious beliefs; hospital mandated it.
• Adaline Deal
  • Service Denied: Heart transplant list at Cincinnati Children’s Hospital (2025).
  • Reason: Parents refused COVID-19 and flu vaccines on religious grounds.
• Jennifer Bridges
  • Service Denied: Employment at Houston Methodist Hospital (2021).
  • Reason: Refused vaccine mandate; fired.
• Northwell Health Employees (1,400 individuals)
  • Service Denied: Employment (2021).
  • Reason: Refused vaccine mandate at New York healthcare provider.
• General Cases of Unemployment Benefit Denials
  • Service Denied: Unemployment benefits (2021-2025).
  • Reason: Fired or quit over vaccine mandates; often deemed “misconduct.”

Hypothesis

Given the time elapsed since the COVID-19 pandemic, can we assess the long-term effectiveness of COVID-19 vaccines through available data ?

Note: This might not be a binary result (even though there are pretty binary sides),

Value	Effect	Description/Correlation-Suggestion
1.0	Positive effect	The vaccine could have a possitive effect in saving lives and getting society back to normal.
0.5	Slightly positive effect	The Vaccine could have a slightly positive effect
0.0	Null/No effect	The Vaccine could have a null/no effect
-0.5	Slightly negative effect	The Vaccine could have a slightly negative effect
-1.0	Negative effect	The Vaccine could have a negative effect

... and of course we might dissagree on the results, and/or further research might need to be done.

below we will be

1. Experimentation obtaining data, and producing some charts/graphs.
   • this can't be a real experiment with a control group, since we are limited to the data,but i hope there is enough data to come to some conclusion.
2. Conclusion drawing the best conclusion we can from the data
3. Replication challening you (the audience) to do approve or disprove the conclusion with your own data science approach.

Setup

In this section, we prepare the notebook by importing necessary libraries, configuring settings, and setting up directories for data and outputs. The setup ensures the environment is ready for data analysis and visualization.

# this code to will import all the things i need for this notebook

import os
import re
import math

import numpy as np
import pandas as pd

# for the notebook rendering 
from IPython.display import display, HTML
from IPython.display import Markdown as MD


# Graphs and Charts
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
# use to export plotly graphs
import plotly.io as pio 

#misc
from scipy.stats import spearmanr, kendalltau
import pycountry

# pandas Settings/Options
pd.set_option("display.max_rows", None) 
pd.set_option("display.max_columns", None)
pd.set_option('display.width', 9000)
pd.set_option('max_colwidth', 400)
pd.set_option('display.float_format', '{:.3f}'.format)

# colormap 
heatmapCM = sns.color_palette('Spectral_r', as_cmap=True)
heatmapCM1 = sns.color_palette('Spectral_r', as_cmap=True)
heatmapCM2 = sns.color_palette('coolwarm', as_cmap=True)
heatmapCM3 = sns.color_palette('viridis_r', as_cmap=True)
heatmapCM4 = sns.color_palette('coolwarm_r', as_cmap=True)

## directories 
DIR = os.getcwd()
print(f'{DIR=}')

DataDIR = os.path.join(DIR,'data')
OutDIR = os.path.join(DIR,'docs')

if not os.path.exists(DataDIR):
    print('***DATA FOLDER IS MISSING***')

if not os.path.exists(OutDIR):
    os.makedirs(OutDIR)

DIR='C:\\Users\\JGarza\\GitHub\\Excess_Mortality_And_Vaccines_In_Europe'

Helping Functions

This section defines utility functions that streamline repetitive tasks and improve code readability. These functions will be used throughout the notebook to simplify operations, enhance modularity, and reduce redundancy.

def df_column_uniquify(df):
    '''
    renames columns that are the same
    '''
    df_columns = df.columns
    new_columns = []
    for item in df_columns:
        counter = 0
        newitem = item
        while newitem in new_columns:
            counter += 1
            newitem = "{}_{}".format(item, counter)
        new_columns.append(newitem)
    df.columns = new_columns
    return df

def abbr_to_isoalpha3(abbr):
    """
    Convert a European country ISO Alpha-2 code to ISO Alpha-3 code.

    Parameters:
        abbreviation (str): ISO Alpha-2 country code (e.g., 'DE' for Germany).

    Returns:
        str: ISO Alpha-3 country code (e.g., 'DEU'), or None if not found.
    """
    try:
        country = pycountry.countries.get(alpha_2=abbr.upper())
        if country:
            return country.alpha_3
        else:
            return None
    except KeyError:
        return None

# this if for converting between the abbreviation andand the names of the countries

country_dict = {
    "BE": "Belgium",
    "BG": "Bulgaria",
    "CZ": "Czechia",
    "DK": "Denmark",
    "DE": "Germany",
    "EE": "Estonia",
    "IE": "Ireland",
    "EL": "Greece",
    "ES": "Spain",
    "FR": "France",
    "HR": "Croatia",
    "IT": "Italy",
    "CY": "Cyprus",
    "LV": "Latvia",
    "LT": "Lithuania",
    "LU": "Luxembourg",
    "HU": "Hungary",
    "MT": "Malta",
    "NL": "Netherlands",
    "AT": "Austria",
    "PL": "Poland",
    "PT": "Portugal",
    "RO": "Romania",
    "SI": "Slovenia",
    "SK": "Slovakia",
    "FI": "Finland",
    "SE": "Sweden",
    "IS": "Iceland",
    "LI": "Liechtenstein",
    "NO": "Norway",
    "CH": "Switzerland",
    "UK": "United Kingdom",
    "ME": "Montenegro",
    "GE": "Georgia",
    "AL": "Albania",
    "RS": "Serbia",
    "AD": "Andorra",
    "AM": "Armenia"
}

def abbr_to_name(abbreviation):
    return country_dict.get(abbreviation.upper(), "Abbreviation not found")

def name_to_abbr(name):
    reverse_dict = {v: k for k, v in country_dict.items()}
    return reverse_dict.get(name, "Unknown")

# testing
print( abbr_to_name("BE") )  # Output: 'Belgium'
print( name_to_abbr("Belgium") )  # Output: 'BE'

Belgium
BE

def bar(num,denom=100.0,length=25,fillchar='#',emptychar='_'):
    fillnum = ((int)( (num/denom) * length))
    return '[' + ( fillnum * fillchar ).ljust(length,emptychar)  + ']' # + f" {(num/denom)*100.0:.2f}%     " 

print(bar(5,50))
print(bar(25,50))
print(bar(40,50))
print(bar(50,50))

[##_______________________]
[############_____________]
[####################_____]
[#########################]

Import and Clean Data

death data

Getting the Data

1. go to Europa.eu - Database
2. choose
   • Population and social conditions
   • Demography, population stock and balance
   • Deaths by week – special data collection
   • Deaths by week, sex and 20-year age group
3. Click the little table 󰓫
4. customize the data
   • Customize your dataset -> Time -> From - to
     • From: 2015-W01
     • To: [Current or Max]
   • Move the Age Class under Geopolitical entity (reporting)
5. Click download (as a spreadsheet) and place the file in the .\data folder

variables

• dd = death data (by year and week)
• ddy = death data (by year)
• ddn = normalized deaths (by year)

# getting the data 
dd = pd.read_excel(os.path.join(DataDIR,"demo_r_mwk_20__custom_15613355_page_spreadsheet.xlsx"),sheet_name = "Sheet 1")

# remove the headers
dd = dd.iloc[7::]

# drop the bad columns
for c in dd.columns:
    if pd.isnull(dd.at[7,c]):
        dd = dd.drop(columns=[c])

# rename time columns
for c in dd.columns:
    name = dd.at[7,c]
    dd = dd.rename(columns={c: name})

# make the duplicate column names unique
dd = df_column_uniquify(dd)

# # rename the first two columns
dd = dd.rename(columns={'TIME': 'abbr'})
dd = dd.rename(columns={'TIME_1':'name'})
dd = dd.rename(columns={'TIME_2':'agegrp'})
dd = dd.rename(columns={'TIME_3':'agegrp_desc'})
           
# drop, replace, reset index,
dd = dd.drop([7,8,9])
dd = dd.replace(to_replace=':', value=None)
dd = dd.reset_index(drop=True)

# display(dd.head(3))
display(dd.tail(10))

C:\Users\JGarza\pythons\Python312\Lib\site-packages\openpyxl\styles\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default
  warn("Workbook contains no default style, apply openpyxl's default")

	abbr	name	agegrp	agegrp_desc	2015-W01	2015-W02	2015-W03	2015-W04	2015-W05	2015-W06	2015-W07	2015-W08	2015-W09	2015-W10	2015-W11	2015-W12	2015-W13	2015-W14	2015-W15	2015-W16	2015-W17	2015-W18	2015-W19	2015-W20	2015-W21	2015-W22	2015-W23	2015-W24	2015-W25	2015-W26	2015-W27	2015-W28	2015-W29	2015-W30	2015-W31	2015-W32	2015-W33	2015-W34	2015-W35	2015-W36	2015-W37	2015-W38	2015-W39	2015-W40	2015-W41	2015-W42	2015-W43	2015-W44	2015-W45	2015-W46	2015-W47	2015-W48	2015-W49	2015-W50	2015-W51	2015-W52	2015-W53	2016-W01	2016-W02	2016-W03	2016-W04	2016-W05	2016-W06	2016-W07	2016-W08	2016-W09	2016-W10	2016-W11	2016-W12	2016-W13	2016-W14	2016-W15	2016-W16	2016-W17	2016-W18	2016-W19	2016-W20	2016-W21	2016-W22	2016-W23	2016-W24	2016-W25	2016-W26	2016-W27	2016-W28	2016-W29	2016-W30	2016-W31	2016-W32	2016-W33	2016-W34	2016-W35	2016-W36	2016-W37	2016-W38	2016-W39	2016-W40	2016-W41	2016-W42	2016-W43	2016-W44	2016-W45	2016-W46	2016-W47	2016-W48	2016-W49	2016-W50	2016-W51	2016-W52	2017-W01	2017-W02	2017-W03	2017-W04	2017-W05	2017-W06	2017-W07	2017-W08	2017-W09	2017-W10	2017-W11	2017-W12	2017-W13	2017-W14	2017-W15	2017-W16	2017-W17	2017-W18	2017-W19	2017-W20	2017-W21	2017-W22	2017-W23	2017-W24	2017-W25	2017-W26	2017-W27	2017-W28	2017-W29	2017-W30	2017-W31	2017-W32	2017-W33	2017-W34	2017-W35	2017-W36	2017-W37	2017-W38	2017-W39	2017-W40	2017-W41	2017-W42	2017-W43	2017-W44	2017-W45	2017-W46	2017-W47	2017-W48	2017-W49	2017-W50	2017-W51	2017-W52	2018-W01	2018-W02	2018-W03	2018-W04	2018-W05	2018-W06	2018-W07	2018-W08	2018-W09	2018-W10	2018-W11	2018-W12	2018-W13	2018-W14	2018-W15	2018-W16	2018-W17	2018-W18	2018-W19	2018-W20	2018-W21	2018-W22	2018-W23	2018-W24	2018-W25	2018-W26	2018-W27	2018-W28	2018-W29	2018-W30	2018-W31	2018-W32	2018-W33	2018-W34	2018-W35	2018-W36	2018-W37	2018-W38	2018-W39	2018-W40	2018-W41	2018-W42	2018-W43	2018-W44	2018-W45	2018-W46	2018-W47	2018-W48	2018-W49	2018-W50	2018-W51	2018-W52	2019-W01	2019-W02	2019-W03	2019-W04	2019-W05	2019-W06	2019-W07	2019-W08	2019-W09	2019-W10	2019-W11	2019-W12	2019-W13	2019-W14	2019-W15	2019-W16	2019-W17	2019-W18	2019-W19	2019-W20	2019-W21	2019-W22	2019-W23	2019-W24	2019-W25	2019-W26	2019-W27	2019-W28	2019-W29	2019-W30	2019-W31	2019-W32	2019-W33	2019-W34	2019-W35	2019-W36	2019-W37	2019-W38	2019-W39	2019-W40	2019-W41	2019-W42	2019-W43	2019-W44	2019-W45	2019-W46	2019-W47	2019-W48	2019-W49	2019-W50	2019-W51	2019-W52	2020-W01	2020-W02	2020-W03	2020-W04	2020-W05	2020-W06	2020-W07	2020-W08	2020-W09	2020-W10	2020-W11	2020-W12	2020-W13	2020-W14	2020-W15	2020-W16	2020-W17	2020-W18	2020-W19	2020-W20	2020-W21	2020-W22	2020-W23	2020-W24	2020-W25	2020-W26	2020-W27	2020-W28	2020-W29	2020-W30	2020-W31	2020-W32	2020-W33	2020-W34	2020-W35	2020-W36	2020-W37	2020-W38	2020-W39	2020-W40	2020-W41	2020-W42	2020-W43	2020-W44	2020-W45	2020-W46	2020-W47	2020-W48	2020-W49	2020-W50	2020-W51	2020-W52	2020-W53	2021-W01	2021-W02	2021-W03	2021-W04	2021-W05	2021-W06	2021-W07	2021-W08	2021-W09	2021-W10	2021-W11	2021-W12	2021-W13	2021-W14	2021-W15	2021-W16	2021-W17	2021-W18	2021-W19	2021-W20	2021-W21	2021-W22	2021-W23	2021-W24	2021-W25	2021-W26	2021-W27	2021-W28	2021-W29	2021-W30	2021-W31	2021-W32	2021-W33	2021-W34	2021-W35	2021-W36	2021-W37	2021-W38	2021-W39	2021-W40	2021-W41	2021-W42	2021-W43	2021-W44	2021-W45	2021-W46	2021-W47	2021-W48	2021-W49	2021-W50	2021-W51	2021-W52	2022-W01	2022-W02	2022-W03	2022-W04	2022-W05	2022-W06	2022-W07	2022-W08	2022-W09	2022-W10	2022-W11	2022-W12	2022-W13	2022-W14	2022-W15	2022-W16	2022-W17	2022-W18	2022-W19	2022-W20	2022-W21	2022-W22	2022-W23	2022-W24	2022-W25	2022-W26	2022-W27	2022-W28	2022-W29	2022-W30	2022-W31	2022-W32	2022-W33	2022-W34	2022-W35	2022-W36	2022-W37	2022-W38	2022-W39	2022-W40	2022-W41	2022-W42	2022-W43	2022-W44	2022-W45	2022-W46	2022-W47	2022-W48	2022-W49	2022-W50	2022-W51	2022-W52	2023-W01	2023-W02	2023-W03	2023-W04	2023-W05	2023-W06	2023-W07	2023-W08	2023-W09	2023-W10	2023-W11	2023-W12	2023-W13	2023-W14	2023-W15	2023-W16	2023-W17	2023-W18	2023-W19	2023-W20	2023-W21	2023-W22	2023-W23	2023-W24	2023-W25	2023-W26	2023-W27	2023-W28	2023-W29	2023-W30	2023-W31	2023-W32	2023-W33	2023-W34	2023-W35	2023-W36	2023-W37	2023-W38	2023-W39	2023-W40	2023-W41	2023-W42	2023-W43	2023-W44	2023-W45	2023-W46	2023-W47	2023-W48	2023-W49	2023-W50	2023-W51	2023-W52	2024-W01	2024-W02	2024-W03	2024-W04	2024-W05	2024-W06	2024-W07	2024-W08	2024-W09	2024-W10	2024-W11	2024-W12	2024-W13	2024-W14	2024-W15	2024-W16	2024-W17	2024-W18	2024-W19	2024-W20	2024-W21	2024-W22	2024-W23	2024-W24	2024-W25	2024-W26	2024-W27	2024-W28	2024-W29	2024-W30	2024-W31	2024-W32	2024-W33	2024-W34	2024-W35	2024-W36	2024-W37	2024-W38	2024-W39	2024-W40	2024-W41	2024-W42	2024-W43	2024-W44	2024-W45	2024-W46	2024-W47	2024-W48	2024-W49	2024-W50	2024-W51	2024-W52	2025-W01	2025-W02	2025-W03	2025-W04	2025-W05	2025-W06
228	AM	Armenia	Y_LT20	Less than 20 years	10	1	18	5	15	21	11	8	9	14	11	7	11	13	5	8	9	13	14	13	10	8	13	11	13	10	17	17	12	4	12	13	15	14	11	16	19	16	11	7	13	6	9	6	17	9	10	14	9	8	14	14	11	6	12	12	12	14	17	12	9	10	6	9	11	13	20	26	22	7	10	12	13	15	13	19	11	8	11	10	16	15	17	8	7	13	6	8	13	13	8	8	10	8	7	15	18	8	13	20	10	17	9	8	10	0	18	12	10	9	7	12	10	10	5	11	7	12	11	12	9	10	13	11	14	5	5	6	12	12	13	11	10	11	7	12	4	12	11	11	11	13	11	14	11	10	7	11	9	2	12	7	15	9	4	8	8	1	11	5	7	12	8	13	11	11	3	13	8	8	11	8	14	11	3	7	16	8	8	11	12	3	10	9	13	17	2	4	8	9	5	7	11	14	10	12	8	3	8	11	10	6	13	4	9	5	5	11	6	0	7	4	8	8	14	3	7	17	10	12	5	5	6	6	3	8	7	7	8	8	9	11	7	9	9	4	13	7	10	6	16	8	9	9	7	4	7	7	9	8	9	7	7	5	7	8	9	6	9	6	6	1	7	9	7	12	11	12	14	10	10	5	11	6	10	6	6	10	12	14	16	8	7	13	6	10	6	8	8	6	11	9	9	6	3	8	10	8	5	4	10	13	20	121	101	124	85	87	86	72	32	30	32	20	28	52	54	51	38	41	33	29	12	7	9	8	7	18	17	11	16	13	11	8	11	16	7	9	12	12	15	17	9	18	12	7	6	14	22	11	11	6	10	5	10	11	6	24	10	4	14	11	18	6	6	8	10	8	11	8	6	8	12	8	15	8	9	6	8	8	12	5	6	5	9	6	10	12	13	7	9	7	6	5	9	8	9	8	6	7	13	3	16	23	19	31	31	14	18	16	7	14	9	14	10	14	8	11	2	9	10	10	11	6	9	8	11	7	14	6	13	15	11	7	9	5	2	10	18	9	7	11	12	7	4	5	9	10	8	6	12	11	8	14	7	17	10	8	3	4	13	8	3	8	9	7	12	6	14	6	7	11	8	4	2	6	7	6	9	5	10	7	6	None	None	None	None	7	6	4	5	7	4	7	10	8	8	9	7	6	10	9	4	7	4	8	6	6	7	4	12	8	11	1	3	11	9	7	8	6	7	10	None	None	None	None	None	None
229	AM	Armenia	Y20-39	From 20 to 39 years	3	3	28	12	18	13	13	18	12	20	15	13	18	14	9	13	15	20	7	11	17	11	14	16	22	8	12	22	20	14	15	17	20	12	22	15	14	12	11	20	17	11	12	9	16	18	15	22	8	22	11	19	9	4	24	17	13	17	20	17	10	6	8	12	17	19	23	26	18	13	10	9	18	15	13	15	12	15	12	17	8	14	15	12	21	10	9	9	19	12	14	13	15	11	18	12	18	25	7	15	14	20	18	11	9	4	22	16	17	13	12	14	16	13	11	16	14	8	9	15	11	11	16	15	15	9	12	18	14	14	14	11	10	11	15	16	12	22	14	13	13	10	4	12	8	7	12	10	11	11	15	13	11	27	7	8	9	1	21	17	12	11	13	10	6	16	3	16	13	19	12	15	16	15	17	13	12	12	10	16	15	12	12	10	12	19	9	10	24	8	20	17	14	14	9	13	16	5	15	17	17	8	14	15	13	16	22	10	16	0	4	10	6	16	7	9	14	16	12	7	14	13	10	13	15	8	11	16	15	11	20	9	4	11	18	10	9	17	9	17	13	5	14	13	14	13	13	11	9	16	11	11	9	11	17	12	10	10	15	11	11	7	12	9	15	15	19	13	10	7	13	16	6	8	7	12	17	6	8	13	14	9	5	15	19	15	15	11	9	17	15	12	16	11	11	17	20	13	17	6	18	23	38	248	233	187	128	150	94	76	42	44	42	35	35	78	58	47	48	51	35	34	21	10	10	20	19	22	26	33	16	24	11	11	13	21	9	13	14	17	16	14	23	21	18	14	19	14	18	20	14	14	13	11	19	16	15	20	21	22	23	12	16	15	16	5	7	17	13	7	10	13	12	10	14	18	9	8	6	11	19	9	9	17	9	17	12	15	10	11	12	13	15	16	11	15	8	13	17	10	20	13	14	30	28	28	28	22	18	15	7	22	13	28	15	17	10	8	9	9	19	23	10	16	18	11	13	11	11	8	16	13	15	14	15	7	8	14	12	7	11	13	15	11	13	8	10	7	8	12	20	19	20	12	9	12	19	12	11	12	11	7	11	13	10	7	10	17	8	11	4	18	16	14	14	13	15	11	14	13	12	16	18	None	None	None	None	20	11	7	10	12	8	7	10	15	20	12	11	10	10	20	7	8	14	12	22	11	9	7	16	15	13	12	11	13	9	16	5	10	16	14	None	None	None	None	None	None
230	AM	Armenia	Y40-59	From 40 to 59 years	34	38	168	108	92	89	82	98	95	99	91	102	97	71	84	85	56	86	92	113	74	56	87	86	91	72	64	75	94	62	75	66	77	65	84	88	60	77	66	72	76	83	74	91	64	90	93	88	82	87	93	90	61	55	146	125	74	103	72	83	97	70	70	86	90	77	78	86	79	78	67	59	73	85	77	69	61	77	77	82	82	73	76	78	80	66	64	76	72	77	87	64	54	84	72	64	75	76	92	96	81	82	90	88	81	30	154	98	65	90	88	83	103	87	77	89	73	54	78	84	78	78	69	49	70	68	60	64	73	62	76	50	71	57	61	65	72	73	62	61	51	58	62	78	76	87	66	66	70	77	75	59	73	75	78	83	57	17	97	92	69	69	94	72	74	79	55	93	66	70	71	81	79	71	70	78	75	72	57	77	67	71	48	62	66	70	63	66	63	74	69	68	68	70	55	73	49	47	73	67	73	66	84	82	76	76	56	81	72	10	47	62	75	83	65	84	99	72	62	83	82	85	72	77	79	60	54	60	86	69	54	61	57	63	67	45	63	70	62	54	60	46	70	76	59	45	55	53	56	64	62	47	63	69	73	76	92	84	75	80	80	37	92	77	95	77	59	79	68	72	71	51	53	63	49	58	58	53	69	62	68	82	52	87	94	83	85	79	78	75	72	77	66	61	55	68	74	72	69	72	54	64	72	138	166	161	143	153	139	124	119	131	110	65	50	161	96	92	81	102	69	65	75	68	72	76	92	72	72	104	87	73	76	80	73	80	56	64	58	78	66	68	67	43	64	57	62	61	66	72	65	80	78	73	93	105	134	124	117	124	90	91	92	86	76	58	59	94	82	68	91	73	87	77	73	54	73	62	62	55	46	49	63	71	58	61	64	55	54	48	50	46	59	50	58	50	54	69	56	59	50	47	56	75	58	62	64	49	62	57	51	55	52	64	59	78	42	50	55	79	63	76	60	61	57	58	66	43	52	58	42	68	59	63	61	51	42	52	49	45	45	55	57	59	39	57	50	70	60	58	72	54	45	68	49	49	57	43	65	57	54	54	37	58	53	40	62	51	46	60	37	73	77	55	63	72	72	62	62	70	59	74	44	None	None	None	None	38	42	44	70	49	60	51	43	55	61	54	51	53	49	68	49	54	55	52	55	45	56	63	46	55	62	51	58	54	59	52	49	45	56	60	None	None	None	None	None	None
231	AM	Armenia	Y60-79	From 60 to 79 years	119	77	491	270	221	273	215	266	268	244	271	263	297	260	245	279	183	255	242	252	217	198	246	227	211	211	185	198	234	180	197	201	243	219	194	156	195	215	189	172	193	205	242	221	236	247	251	247	214	271	211	279	163	151	423	326	254	278	292	235	236	214	212	228	231	227	210	198	198	232	209	212	216	203	220	218	200	206	212	201	197	220	193	187	186	202	205	191	177	191	188	169	268	215	253	223	221	228	231	245	256	276	288	347	235	97	456	306	295	251	237	253	247	243	254	273	242	232	243	200	234	223	229	191	251	196	206	210	195	199	194	200	215	194	211	216	223	200	195	145	180	184	170	175	175	219	210	190	222	229	193	226	222	222	242	225	255	88	351	221	248	228	224	228	223	202	161	276	199	211	206	203	227	219	191	209	199	178	161	202	199	177	162	217	264	234	171	193	196	187	178	175	169	184	134	183	193	160	236	192	186	202	232	232	226	191	224	205	238	52	224	228	243	229	232	218	237	223	195	227	239	222	210	198	204	180	198	183	216	215	225	194	188	189	166	155	191	183	186	210	178	199	194	168	152	180	184	228	184	183	163	179	184	221	217	197	217	220	228	222	274	117	291	284	231	256	238	246	240	240	221	225	227	144	179	202	221	189	212	194	237	216	153	249	256	281	283	273	282	246	260	203	217	201	200	203	204	199	209	203	165	209	233	339	407	512	533	556	552	478	428	372	382	254	212	482	307	290	269	257	257	217	257	221	283	310	338	320	360	333	378	285	261	239	221	210	234	206	219	245	211	211	203	213	186	227	231	226	235	235	264	289	270	289	373	396	459	550	495	492	388	341	332	321	285	192	235	340	265	243	289	276	337	316	288	233	246	198	213	193	202	178	233	214	183	205	221	179	192	217	190	146	147	189	191	214	173	182	179	199	200	217	195	175	209	183	172	171	207	212	205	223	208	202	217	234	231	200	220	291	255	250	213	224	233	239	206	189	212	214	235	211	197	210	206	209	198	193	183	189	196	198	194	186	191	197	198	181	208	217	242	191	203	202	160	176	202	200	215	223	194	213	246	235	213	214	216	239	237	244	214	309	268	264	234	256	268	255	269	222	244	233	219	None	None	None	None	215	229	202	255	237	221	238	216	196	188	214	222	199	208	204	210	205	226	215	219	215	207	227	220	234	244	231	230	274	214	219	263	229	245	256	None	None	None	None	None	None
232	AM	Armenia	Y_GE80	80 years or over	83	66	389	266	214	219	190	226	244	201	215	233	260	218	242	213	144	219	211	209	184	196	198	177	186	203	190	186	184	156	175	182	174	203	178	125	153	164	164	177	145	146	202	184	172	184	190	226	179	220	238	241	146	124	376	291	211	251	236	237	204	164	215	195	199	195	187	223	209	193	206	193	190	187	213	194	186	198	189	174	162	212	195	148	179	204	155	170	160	142	163	163	177	172	196	202	178	205	242	222	236	283	285	330	278	84	464	304	277	255	275	236	262	230	202	230	252	223	229	272	213	193	247	168	212	186	195	207	197	187	179	183	183	175	196	188	230	214	180	165	147	153	137	153	147	173	191	223	217	221	214	187	214	211	215	222	223	64	356	278	256	222	253	243	248	192	182	260	207	200	157	158	173	188	203	196	179	179	180	177	178	180	198	244	253	236	160	167	187	159	160	147	177	153	141	167	179	122	205	211	199	185	223	222	227	216	204	189	205	52	215	273	265	244	237	243	210	220	227	261	231	242	199	236	190	197	212	237	253	211	175	211	185	190	237	147	223	196	197	186	176	172	195	184	142	149	167	195	184	193	172	205	192	210	192	211	241	209	265	191	262	90	291	256	263	219	263	245	243	280	275	251	227	172	203	218	219	200	206	232	224	249	181	299	259	242	263	264	280	209	290	259	199	198	217	172	185	198	164	194	177	200	228	275	366	501	483	540	515	450	401	388	384	270	194	503	290	268	265	281	260	215	247	209	241	254	279	292	301	328	318	267	265	239	217	237	212	211	216	277	213	195	190	178	162	202	198	221	231	210	228	239	243	269	313	341	369	433	445	425	384	334	297	290	272	227	270	352	284	241	315	352	433	493	396	301	269	243	218	235	176	163	206	186	162	203	184	173	190	184	171	138	146	196	201	211	159	181	174	188	171	178	161	165	210	159	174	164	154	183	177	176	182	173	172	203	184	222	183	261	219	227	220	195	201	203	213	216	227	214	200	176	190	183	171	168	176	177	158	154	142	155	141	154	136	144	155	152	161	165	200	184	154	148	129	124	164	156	185	151	198	175	182	207	181	169	176	205	172	217	167	258	221	220	235	223	220	204	208	197	244	186	180	None	None	None	None	197	163	134	191	154	164	187	177	185	137	164	154	157	148	181	173	192	165	159	165	145	148	174	162	161	184	175	191	180	217	186	191	183	214	206	None	None	None	None	None	None
233	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
234	Special value	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
235	None	not available	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
236	Observation flags:	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
237	p	provisional	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

# This code processes the raw death data (dd) by restructuring it into a long-form dataframe. 
# Each row in the new dataframe represents a single country's deaths for a specific year and week, 
# along with additional metadata such as country abbreviations and derived values.

temp = dd.melt(id_vars=['name','abbr','agegrp','agegrp_desc'],var_name='year-week',value_name='deaths')
temp['year'] = pd.to_numeric(temp['year-week'].str[0:4])
temp['week'] = pd.to_numeric(temp['year-week'].str[6:8])
temp['year.week'] = temp['year'] + temp['week']/100
temp['year.p'] = temp['year'] + (temp['week']/53.001)
dd = temp

# lets remove some data we don't need

# this is a combination of 27 countries
dd = dd[dd['abbr']!= 'EU27_2020'] 


dd = dd[dd['abbr']!= 'not available']
dd = dd[dd['abbr']!= 'Special value']
dd = dd[dd['abbr']!= 'None']
dd = dd[dd['abbr']!= 'Observation flags:']
dd = dd[dd['abbr']!= 'p']

# too early for 2025
dd = dd[dd['year']!= 2025]

dd = dd[~dd['name'].isna()]
dd = dd[~dd['agegrp'].isna()]

# we don't need these columns 
dd.drop(columns=['agegrp_desc'], inplace=True)
dd.drop(columns=['year-week'], inplace=True)

# converting columns 
dd['deaths'] = pd.to_numeric(dd['deaths'])

# there are quite a few NAN (not a number) values
# here we get rid of them 

# Get counts of NA values for each 'abbr' group
na_counts = dd[dd.deaths.isna()].groupby(['abbr','agegrp']).size()

# Filter for 'abbr' groups with more than 12 NA values
filtered_abbrs = na_counts[na_counts > 12*5].index

for abbr, agegrp in filtered_abbrs:
    print(f'removing -- {abbr} {abbr_to_name(abbr)} {agegrp} NACount={na_counts[(abbr, agegrp)]}')
    # Corrected filtering condition: remove rows where abbr and agegrp match separately
    dd = dd[~((dd.abbr == abbr) & (dd.agegrp == agegrp))]  # Use bitwise AND & inside the negation
    # print(len(dd))

removing -- AD Andorra TOTAL NACount=261
removing -- AD Andorra Y20-39 NACount=261
removing -- AD Andorra Y40-59 NACount=261
removing -- AD Andorra Y60-79 NACount=261
removing -- AD Andorra Y_GE80 NACount=261
removing -- AD Andorra Y_LT20 NACount=261
removing -- AL Albania TOTAL NACount=171
removing -- AL Albania Y20-39 NACount=171
removing -- AL Albania Y40-59 NACount=171
removing -- AL Albania Y60-79 NACount=171
removing -- AL Albania Y_GE80 NACount=171
removing -- AL Albania Y_LT20 NACount=171
removing -- GE Georgia TOTAL NACount=208
removing -- GE Georgia Y20-39 NACount=208
removing -- GE Georgia Y40-59 NACount=208
removing -- GE Georgia Y60-79 NACount=208
removing -- GE Georgia Y_GE80 NACount=208
removing -- GE Georgia Y_LT20 NACount=208
removing -- IE Ireland Y20-39 NACount=522

removing -- IE Ireland Y40-59 NACount=522
removing -- IE Ireland Y60-79 NACount=522
removing -- IE Ireland Y_GE80 NACount=522
removing -- IE Ireland Y_LT20 NACount=522
removing -- UK United Kingdom TOTAL NACount=210
removing -- UK United Kingdom Y20-39 NACount=210
removing -- UK United Kingdom Y40-59 NACount=211
removing -- UK United Kingdom Y60-79 NACount=210
removing -- UK United Kingdom Y_GE80 NACount=210
removing -- UK United Kingdom Y_LT20 NACount=210

# Fill missing values (NaNs) in the 2024 data with the mean deaths for 2024 by country

# Filter data for the year 2024
df_2024 = dd[dd.year == 2024]

# Group the 2024 data by country abbreviation ('abbr') and calculate summary statistics
grouped = df_2024.groupby(['abbr','agegrp'])

# Compute statistics: mean, total count, number of NaNs, total deaths, and mean deaths per week
stats = grouped['deaths'].agg(
    Mean='mean',                        # Average deaths for each country in 2024
    Count='size',                       # Total number of records for each country in 2024
    NACount=lambda x: x.isna().sum(),   # Count of missing (NaN) values
    SumFor2024='sum'                    # Total deaths for each country in 2024
).reset_index()

# Replace missing death values in 2024 with the calculated mean for each country
for _, row in stats.iterrows():
    dd.loc[
        (dd.abbr == row['abbr']) & (dd.agegrp == row['agegrp']) & (dd.year == 2024) & (dd.deaths.isna()), 
        'deaths'
    ] = row['Mean']

# we are going to create a new age group, everyone less than 80
# LT80 is less than 80 years old

# exclude where the age is greater than 80
temp = dd.copy()
temp = temp[temp['agegrp'] != 'TOTAL']
temp = temp[temp['agegrp'] != 'Y_GE80']

temp = pd.pivot_table(
    temp, 
    values='deaths', 
    index=['name', 'abbr','year','week','year.week','year.p'], 
    aggfunc='sum'
)
temp = temp.reset_index()

temp['agegrp'] = 'LT80'

dd = pd.concat([dd,temp])

# checking the data
display(dd.head(5))
display(dd.tail(5))

	name	abbr	agegrp	deaths	year	week	year.week	year.p
5	Belgium	BE	TOTAL	2461.000	2015	1	2015.010	2015.019
6	Belgium	BE	Y_LT20	19.000	2015	1	2015.010	2015.019
7	Belgium	BE	Y20-39	34.000	2015	1	2015.010	2015.019
8	Belgium	BE	Y40-59	191.000	2015	1	2015.010	2015.019
9	Belgium	BE	Y60-79	756.000	2015	1	2015.010	2015.019

	name	abbr	agegrp	deaths	year	week	year.week	year.p
17221	Switzerland	CH	LT80	420.000	2024	48	2024.480	2024.906
17222	Switzerland	CH	LT80	414.000	2024	49	2024.490	2024.925
17223	Switzerland	CH	LT80	466.000	2024	50	2024.500	2024.943
17224	Switzerland	CH	LT80	472.000	2024	51	2024.510	2024.962
17225	Switzerland	CH	LT80	501.000	2024	52	2024.520	2024.981

# save to out folder
dd.to_csv(r'out\death_data.csv',index=False)

ddy = pd.pivot_table(
    dd,
    values='deaths',
    index=['name', 'abbr','agegrp','year'], 
    aggfunc='sum'
)
ddy = ddy.reset_index()

display(ddy.head(5))
display(ddy.tail(5))

# save to out folder
ddy.to_csv(r'out\death_data_year.csv',index=False)

	name	abbr	agegrp	year	deaths
0	Armenia	AM	LT80	2015	17785.000
1	Armenia	AM	LT80	2016	17334.000
2	Armenia	AM	LT80	2017	16426.000
3	Armenia	AM	LT80	2018	15379.000
4	Armenia	AM	LT80	2019	14880.000

	name	abbr	agegrp	year	deaths
2315	Switzerland	CH	Y_LT20	2020	491.000
2316	Switzerland	CH	Y_LT20	2021	468.000
2317	Switzerland	CH	Y_LT20	2022	511.000
2318	Switzerland	CH	Y_LT20	2023	451.000
2319	Switzerland	CH	Y_LT20	2024	399.000

# here we normalize the death values

ddn = ddy.copy()
ddn['deaths_norm'] = 0.0

## baseline years are 2015,2016,2017 before the pandemic 
blyears = ddn[ddn.year.isin([2015,2016,2017])]
grouped = blyears.groupby(['abbr','agegrp'])

# Compute baseline mean
temp = grouped['deaths'].agg(
    baseline='mean',
).reset_index()

# Merge baseline means with the original DataFrame
ddn = ddn.merge(temp, on=['abbr', 'agegrp'], how='left')

# Normalize deaths column
ddn['deaths_norm'] = ddn['deaths'] / ddn['baseline']

# Drop the intermediate baseline column if not needed
ddn.drop(columns=['baseline'], inplace=True)

# save to out folder
ddn.to_csv(r'out\death_data_norm.csv',index=False)

cause of death

Getting the Data

1. go to Europa.eu - Database
2. choose
   • Population and social conditions
   • Health
   • Causes of death
   • General mortality
   • Causes of death - deaths by country of residence and occurrence
3. Click the little table 󰓫
4. customize the data
   • Customize your dataset -> Time -> From - to
     • From: 2015
     • To: [Current or Max]
   • Customize your dataset -> International Statistical Classification of Diseases and Related Health Problems (ICD-10 2010)
     • All
       • UnCheck All
     • Level 1
       • All Checked

       • Level 1 is basic classification of the cause of death

   • Move the ``International Statistical Classification...underGeopolitical entity (reporting)`
5. Click download (as a spreadsheet) and place the file in the .\data folder

variables

• cod = cause of death data
• codn = cod, normalized

# getting the data 

# Level 1
cod = pd.read_excel(os.path.join(DataDIR,"hlth_cd_aro__custom_15699666_page_spreadsheet.xlsx"),sheet_name = "Sheet 1")

# Aggregate 
# cod = pd.read_excel(os.path.join(DataDIR,"hlth_cd_aro__custom_15989358_page_spreadsheet.xlsx"),sheet_name = "Sheet 1")

# remove the headers
cod = cod.iloc[9::]

# drop the bad columns
for c in cod.columns:
    if pd.isnull(cod.at[9,c]):
        cod = cod.drop(columns=[c])

# rename time columns
for c in cod.columns:
    name = cod.at[9,c]
    cod = cod.rename(columns={c: name})

# make the duplicate column names unique
cod = df_column_uniquify(cod)

# # rename the first two columns
cod = cod.rename(columns={'TIME': 'name'})
cod = cod.rename(columns={'TIME_1':'cod'})
           
# drop, replace, reset index,
cod = cod.drop([9,10])
cod = cod.replace(to_replace=':', value=None)
cod = cod.reset_index(drop=True)

cod['abbr'] = cod['name'].apply(name_to_abbr)

# display(cod.head(5))

C:\Users\JGarza\pythons\Python312\Lib\site-packages\openpyxl\styles\stylesheet.py:237: UserWarning: Workbook contains no default style, apply openpyxl's default
  warn("Workbook contains no default style, apply openpyxl's default")

# This code processes the raw cod data (cod) by restructuring it into a long-form dataframe. 
# along with additional metadata such as country abbreviations and derived values.

temp = cod.melt(id_vars=['name','abbr','cod'],var_name='year',value_name='deaths')
temp['year'] = pd.to_numeric(temp['year'])
cod = temp

# display(cod.head(10))

# lets remove some data we don't need

# this is a combination of 27 countries
cod.loc[cod['name']== 'European Union - 27 countries (from 2020)','abbr'] = 'Euro27'

cod = cod[cod['name']!= 'not available']
cod = cod[cod['name']!= 'Special value']
cod = cod[cod['name']!= 'None']
cod = cod[cod['name']!= 'Observation flags:']
cod = cod[cod['name']!= 'p']
cod = cod[cod['name']!= 'Nan']
cod = cod[cod['name']!= 'd']

cod = cod[~cod['name'].isna()]

# removed due to lack of reporting data 
cod = cod[cod['abbr']!= 'Euro27']
cod = cod[cod['abbr']!= 'Unknown']
cod = cod[cod['abbr']!= 'UK']
cod = cod[cod['abbr']!= 'LI']


# display(cod.tail(10))

# if a cause of death is null, we'll fill it with 0

# cod['deaths'].fillna(0, inplace=True) #deprecated
cod['deaths'] = pd.to_numeric(cod['deaths'], errors='coerce').fillna(0).astype(int)

# save to out folder
cod.to_csv(r'out\cod_data.csv',index=False)

# here we normalize the cod data 

codn = cod.copy()
codn['deaths_norm'] = np.nan

## baseline years are 2015,2016,2017 before the pandemic 
blyears = codn[codn.year.isin([2015,2016,2017])]
grouped = blyears.groupby(['name','cod'])

# Compute baseline mean
temp = grouped['deaths'].agg(
    baseline='mean',
).reset_index()

# Merge baseline means with the original DataFrame
codn = codn.merge(temp, on=['name', 'cod'], how='left')

codn['baseline'] = codn['baseline'].replace(0, np.nan)

# set the death_norm to deaths/balseline
# * if the baseline is not NA
# * if the deaths is not NA
codn.loc[(~codn['baseline'].isna()) & (~codn['deaths'].isna()), 'deaths_norm'] = codn['deaths'] / codn['baseline']

# Drop the intermediate baseline column if not needed
codn.drop(columns=['baseline'], inplace=True)

# # save to out folder
codn.to_csv(r'out\cod_data_norm.csv',index=False)

# display(codn.head(1000))

vaccine data

Getting the Data

1. Go to https://www.ecdc.europa.eu/
2. Click the Download in CSV.

variables

• vd = vaccine data

# import Vaccine Data 
vd = pd.read_csv(os.path.join(DataDIR,'data.csv'))
vd = vd[vd['TargetGroup'] == 'ALL']

# create a year column
vd['year'] = pd.to_numeric(vd['YearWeekISO'].str[0:4])

# renaming columns for shorter names
vd = vd.rename(columns={'ReportingCountry':'abbr'})
vd = vd.rename(columns={'Vaccine':'vacc'})
vd = vd.rename(columns={'FirstDose':'dose1'})
vd = vd.rename(columns={'SecondDose':'dose2'})
vd = vd.rename(columns={'DoseAdditional1':'doesA1'})
vd = vd.rename(columns={'DoseAdditional2':'doesA2'})
vd = vd.rename(columns={'DoseAdditional3':'doesA3'})
vd = vd.rename(columns={'DoseAdditional4':'doesA4'})
vd = vd.rename(columns={'DoseAdditional5':'doesA5'})
vd = vd.rename(columns={'UnknownDose':'doseUNK'})

doseCol = ['dose1','dose2','doesA1','doesA2','doesA3','doesA4','doesA5','doseUNK']
# calculate the sum of all the Doses
vd = pd.pivot_table(
    data = vd,
    values = doseCol,
    index = ['abbr','year','Population','vacc'],
    aggfunc="sum"
    )
vd = vd.reset_index()

# gets get the total doses given 
vd['total_dose'] = vd[doseCol].sum(axis=1)
doseCol.append('total_dose')

# lets make a new record for all vaccines
temp = pd.pivot_table(
    data = vd,
    values = doseCol,
    index = ['abbr','year','Population'],
    aggfunc="sum"
    )
temp = temp.reset_index()
temp['vacc'] = 'All'

# and add it to all the vaccine data
vd = pd.concat([vd,temp])


# gets get the total dose1 (first dose) given 
vd['total_dose1'] = vd['dose1']

# dose1 ... cumulative sum, dose1/pop. , dose1/pop. normalize 
vd['td1_sum'] = vd.groupby(['abbr','vacc'])['total_dose1'].cumsum()
vd['dose1_pop_ratio'] = vd['td1_sum']/vd['Population']
## normalizsed version
vd['dpr1_norm'] = (vd['dose1_pop_ratio'] - vd['dose1_pop_ratio'].min()) / (vd['dose1_pop_ratio'].max() - vd['dose1_pop_ratio'].min())

vd['td_sum'] = vd.groupby(['abbr','vacc'])['total_dose'].cumsum()
vd['dose_pop_ratio'] = vd['td_sum']/vd['Population']
## normalizsed version
vd['dpr_norm'] = (vd['dose_pop_ratio'] - vd['dose_pop_ratio'].min()) / (vd['dose_pop_ratio'].max() - vd['dose_pop_ratio'].min())

vd['name'] = vd.abbr.apply(abbr_to_name)

# display(vd.head(5))
# display(vd.tail(5))

# save to out folder
vd.to_csv(r'out\vacc_data.csv',index=False)

# display(vd.head(100))

# an example of a few 
display(vd[(vd['abbr']=='FI') & (vd['vacc']=='All')])
display(vd[(vd['abbr']=='SK') & (vd['vacc']=='All')])
display(vd[(vd['abbr']=='RO') & (vd['vacc']=='All')])

	abbr	year	Population	vacc	doesA1	doesA2	doesA3	doesA4	doesA5	dose1	dose2	doseUNK	total_dose	total_dose1	td1_sum	dose1_pop_ratio	dpr1_norm	td_sum	dose_pop_ratio	dpr_norm	name
40	FI	2020	5525292	All	0	0	0	0	0	17151	0	0	17151	17151	17151	0.003	0.001	17151	0.003	0.000	Finland
41	FI	2021	5533793	All	3835791	2706	0	0	0	12062376	11587069	0	27487942	12062376	12079527	2.183	0.990	27505093	4.970	0.711	Finland
42	FI	2022	5548241	All	5333235	3641874	1426157	0	0	153691	359625	0	10914582	153691	12233218	2.205	1.000	38419675	6.925	0.990	Finland
43	FI	2023	5548241	All	21628	143580	209551	0	0	3285	4192	0	382236	3285	12236503	2.205	1.000	38801911	6.994	1.000	Finland

	abbr	year	Population	vacc	doesA1	doesA2	doesA3	doesA4	doesA5	dose1	dose2	doseUNK	total_dose	total_dose1	td1_sum	dose1_pop_ratio	dpr1_norm	td_sum	dose_pop_ratio	dpr_norm	name
114	SK	2020	5457873	All	0	0	0	0	0	4536	5	0	4541	4536	4536	0.001	0.000	4541	0.001	0.000	Slovakia
115	SK	2021	5459781	All	1103260	5	0	0	0	2654986	2400025	0	6158276	2654986	2659522	0.487	0.221	6162817	1.129	0.161	Slovakia
116	SK	2022	5434712	All	573964	73532	669	0	0	45368	92061	0	785594	45368	2704890	0.498	0.226	6948411	1.279	0.183	Slovakia
117	SK	2023	5434712	All	634	4719	486	0	0	367	296	0	6502	367	2705257	0.498	0.226	6954913	1.280	0.183	Slovakia

	abbr	year	Population	vacc	doesA1	doesA2	doesA3	doesA4	doesA5	dose1	dose2	doseUNK	total_dose	total_dose1	td1_sum	dose1_pop_ratio	dpr1_norm	td_sum	dose_pop_ratio	dpr_norm	name
103	RO	2021	19201662	All	1140530	0	0	0	0	7734378	5677069	0	14551977	7734378	7734378	0.403	0.183	14551977	0.758	0.108	Romania
104	RO	2022	19042455	All	611197	25319	0	0	0	181348	184394	0	1002258	181348	7915726	0.416	0.188	15554235	0.817	0.117	Romania
105	RO	2023	19042455	All	2714	6420	0	0	0	2219	1678	0	13031	2219	7917945	0.416	0.189	15567266	0.818	0.117	Romania

combine all data

variables

cd = combined data

cols = ['name','abbr', 'year','source', 'filter','value_type','value']

cd = pd.DataFrame(columns=cols)


# deaths data 
temp = ddy.copy()

temp['source'] = 'deaths'
temp['filter'] = temp['agegrp']
temp['value_type'] = 'value'
temp['value'] = temp['deaths']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])

# deaths data normalized 

temp = ddn.copy()

temp['source'] = 'deaths'
temp['filter'] = temp['agegrp']
temp['value_type'] = 'normalized'
temp['value'] = temp['deaths_norm']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])

# cause of death data

temp = cod.copy()

temp['source'] = 'cause_of_death'
temp['filter'] = temp['cod']
temp['value_type'] = 'value'
temp['value'] = temp['deaths']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])


# cause of death data normalized

temp = codn.copy()

temp['source'] = 'cause_of_death'
temp['filter'] = temp['cod']
temp['value_type'] = 'normalized'
temp['value'] = temp['deaths_norm']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])


# Vaccine Data - total_dose1

temp = vd.copy()

temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'value (first dose)'
temp['value'] = temp['total_dose1']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])

# Vaccine Data - total_dose1/pop

temp = vd.copy()

temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'ratio (first dose/pop.)'
temp['value'] = temp['dose1_pop_ratio']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])


# Vaccine Data - total_dose1/pop normalizsed

temp = vd.copy()

temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'normalized (first dose/pop.)'
temp['value'] = temp['dpr1_norm']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])


# Vaccine Data - total_dose

temp = vd.copy()

temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'value (total_dose)'
temp['value'] = temp['total_dose']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])

# Vaccine Data - dose_pop_ratio
temp = vd.copy()

temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'ratio (total_dose/pop.)'
temp['value'] = temp['dose_pop_ratio']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])

# Vaccine Data - dpr_norm
temp = vd.copy()

temp['source'] = 'vaccine'
temp['filter'] = temp['vacc']
temp['value_type'] = 'normalized (total_dose/pop.)'
temp['value'] = temp['dpr_norm']

for c in temp.columns:
    if c not in cols:
        temp = temp.drop(columns=[c])

cd = pd.concat([cd,temp])


display(cd.tail(5))
display(cd[(cd['source'] == 'deaths') & (cd['value_type'] == 'normalized')].tail(5))

# save to out folder
cd.to_csv(r'out\combined_data.csv',index=False)

C:\Users\JGarza\AppData\Local\Temp\ipykernel_27628\3464685614.py:18: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.
  cd = pd.concat([cd,temp])

	name	abbr	year	source	filter	value_type	value
113	Slovenia	SI	2023	vaccine	All	normalized (total_dose/pop.)	0.197
114	Slovakia	SK	2020	vaccine	All	normalized (total_dose/pop.)	0.000
115	Slovakia	SK	2021	vaccine	All	normalized (total_dose/pop.)	0.161
116	Slovakia	SK	2022	vaccine	All	normalized (total_dose/pop.)	0.183
117	Slovakia	SK	2023	vaccine	All	normalized (total_dose/pop.)	0.183

	name	abbr	year	source	filter	value_type	value
2315	Switzerland	CH	2020	deaths	Y_LT20	normalized	0.974
2316	Switzerland	CH	2021	deaths	Y_LT20	normalized	0.929
2317	Switzerland	CH	2022	deaths	Y_LT20	normalized	1.014
2318	Switzerland	CH	2023	deaths	Y_LT20	normalized	0.895
2319	Switzerland	CH	2024	deaths	Y_LT20	normalized	0.792

sources = cd['source'].drop_duplicates().to_list()

for s in sources:
    print('source: ',s)
    filters = cd[cd['source']==s]['filter'].drop_duplicates().to_list()
    print('filter:')
    print('\t',filters)
    value_types = cd[cd['source']==s]['value_type'].drop_duplicates().to_list()
    print('value_type:')
    print('\t',value_types)
    print('---')

source:  deaths
filter:
	 ['LT80', 'TOTAL', 'Y20-39', 'Y40-59', 'Y60-79', 'Y_GE80', 'Y_LT20']
value_type:
	 ['value', 'normalized']
---
source:  cause_of_death
filter:
	 ['Malignant neoplasms (C00-C97)', 'Endocrine, nutritional and metabolic diseases (E00-E90)', 'Mental and behavioural disorders (F00-F99)', 'Diseases of the circulatory system (I00-I99)', 'Diseases of the respiratory system (J00-J99)', 'Diseases of the digestive system (K00-K93)', 'Diseases of the skin and subcutaneous tissue (L00-L99)', 'Diseases of the musculoskeletal system and connective tissue (M00-M99)', 'Diseases of the genitourinary system (N00-N99)', 'Pregnancy, childbirth and the puerperium (O00-O99)', 'Certain conditions originating in the perinatal period (P00-P96)', 'Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)', 'Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)']
value_type:
	 ['value', 'normalized']
---
source:  vaccine
filter:
	 ['AZ', 'COM', 'COMBA.1', 'COMBA.4-5', 'JANSS', 'MOD', 'MODBA.1', 'NVXD', 'SGSK', 'UNK', 'VLA', 'MODBA.4-5', 'COMXBB', 'COMBIV', 'MODBIV', 'BECNBG', 'SPU', 'SIN', 'BHACOV', 'All']
value_type:
	 ['value (first dose)', 'ratio (first dose/pop.)', 'normalized (first dose/pop.)', 'value (total_dose)', 'ratio (total_dose/pop.)', 'normalized (total_dose/pop.)']
---

Lets Visualize the Data

Line Charts - normalized deaths

• Note the chart below shows that all the countries went through the pandemic 2020-2021, however some countries recovered (1.0 or less), and other countries still have higher than normal death rates (over 1.0).

title = 'Line Chart - normalized deaths in Europe 2015-2024 (by year)'
display(MD(f'### {title}'))

temp = cd[(cd['source']=='deaths') & (cd['filter']=='TOTAL') & (cd['value_type']=='normalized')]
temp = temp.sort_values(by='value', ascending=True)
temp = temp.sort_values(by='year', ascending=True)

fig = px.line(
    temp,
    x='year',
    y='value',
    color='name',
    height=750 ,
    hover_data={
        'name', 'abbr', 'value'
    },
    title=title
    )
fig.update_layout(template="plotly_dark")

Line Chart - normalized deaths in Europe 2015-2024 (by year)

title = 'Line Chart - normalized deaths in Europe 2015-2024 (by year)'
display(MD(f'### {title}'))

subtitle = 'Only the top 5 values in 2024, and the bottom 5 values in 2024'
display(MD(f'#### {subtitle}'))

temp = cd[(cd['source']=='deaths') & (cd['filter']=='TOTAL') & (cd['value_type']=='normalized')]

head2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).head(5)
tail2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).tail(5)


temp = temp[
    (temp['name'].isin(head2024.name))
    | (temp['name'].isin(tail2024.name))
    ]


temp = temp.sort_values(by='value', ascending=True)
temp = temp.sort_values(by='year', ascending=True)

fig = px.line(
    temp,
    x='year',
    y='value',
    color='name',
    height=750 ,
    hover_data={
        'name', 'abbr', 'value'
    },
    title=title
    )
fig.update_layout(template="plotly_dark")

Line Chart - normalized deaths in Europe 2015-2024 (by year)

Only the top 5 values in 2024, and the bottom 5 values in 2024

• Note here we can see some data might not be trust-worthy. see Liechtenstein

title = 'Line Chart - normalized deaths in Europe 2015-2024 (by year)'
display(MD(f'### {title}'))

display(MD(f'#### Only the top 5 values in 2024, and the bottom 5 values in 2024'))
display(MD(f'#### Only where the deaths are from people who are less than 80 years old'))

temp = cd[(cd['source']=='deaths') & (cd['filter']=='LT80') & (cd['value_type']=='normalized')]

head2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).head(5)
tail2024 = temp[(temp['year']==2024)].sort_values(by='value', ascending=False).tail(5)


temp = temp[
    (temp['name'].isin(head2024.name))
    | (temp['name'].isin(tail2024.name))
    ]

temp = temp.sort_values(by='value', ascending=True)
temp = temp.sort_values(by='year', ascending=True)


fig = px.line(
    temp,
    x='year',
    y='value',
    color='name',
    height=750 ,
    hover_data={
        'name', 'abbr', 'value'
    },
    title=title
    )
fig.update_layout(template="plotly_dark")

Line Chart - normalized deaths in Europe 2015-2024 (by year)

Only the top 5 values in 2024, and the bottom 5 values in 2024

Only where the deaths are from people who are less than 80 years old

HeatMaps of deaths by AgeGroups

display(MD('### HeatMap (of deaths based on age groups)'))

agegrps = cd[(cd['source']=='deaths')]['filter'].drop_duplicates().to_list()

for ag in agegrps:
    display(MD(f'#### {ag}'))
    temp = cd[(cd['source']=='deaths') & (cd['filter']==ag) & (cd['value_type']=='normalized')]

    temp = pd.pivot_table(
        data = temp,
        values = 'value',
        index = ['abbr','name','filter','value_type'],
        columns=['year'],
        aggfunc='mean',
    )
    temp.columns.name = 'index' 
    temp = temp.reset_index()

    ## sum of post pandemic deaths
    sppd = pd.pivot_table(
        cd[(cd['source']=='deaths') & (cd['filter']==ag) & (cd['value_type']=='normalized') & (cd['year'] > 2023)], # totals only 
        values = 'value',
        index = ['abbr','name'],
        aggfunc='sum',
    )
    sppd = sppd.reset_index()
    sppd = sppd.sort_values(by='value',ascending=False)
    
    temp = temp.set_index('name').reindex(sppd['name']).reset_index()

    display(temp.style.background_gradient(cmap=heatmapCM,axis=1))

HeatMap (of deaths based on age groups)

LT80

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Iceland	IS	LT80	normalized	0.963504	1.036496	1.000000	1.019812	1.012513	1.105318	1.123045	1.273201	1.211679	1.234619
1	Malta	MT	LT80	normalized	1.017293	0.967203	1.015504	1.057841	1.036971	1.166369	1.196780	1.207513	1.124031	1.163387
2	Netherlands	NL	LT80	normalized	1.009040	0.998123	0.992837	1.013964	1.004492	1.114691	1.143528	1.122958	1.113380	1.110753
3	Norway	NO	LT80	normalized	1.003819	0.998207	0.997974	1.013113	1.015627	1.033864	1.050348	1.137911	1.107398	1.106405
4	Liechtenstein	LI	LT80	normalized	0.876923	1.107692	1.015385	1.007692	0.946154	1.115385	0.976923	0.000000	0.953846	1.092308
5	France	FR	LT80	normalized	1.012897	0.989268	0.997835	1.006304	1.003646	1.094593	1.112343	1.124722	1.086984	1.085468
6	Cyprus	CY	LT80	normalized	1.026756	0.958661	1.014583	1.020289	1.053005	1.140122	1.280878	1.213923	1.143546	1.070885
7	Spain	ES	LT80	normalized	1.029906	0.983827	0.986268	0.997999	0.994437	1.164643	1.109420	1.114142	1.082376	1.068357
8	Finland	FI	LT80	normalized	1.000246	1.000328	0.999426	0.998402	0.988075	1.026145	1.040611	1.090771	1.100033	1.050775
9	Luxembourg	LU	LT80	normalized	0.979849	0.945681	1.074470	1.028211	1.006658	1.077098	1.061854	1.024006	1.026108	1.037147
10	Poland	PL	LT80	normalized	1.017801	0.981581	1.000618	1.023203	1.018394	1.181092	1.309963	1.115682	1.035903	1.026510
11	Belgium	BE	LT80	normalized	1.029200	0.986823	0.983978	0.983892	0.964447	1.091920	1.040751	1.042612	1.006225	1.008963
12	Austria	AT	LT80	normalized	1.016921	0.987902	0.995178	1.019837	1.002171	1.082151	1.076318	1.062049	1.020941	1.007465
13	Slovakia	SK	LT80	normalized	1.024468	0.976413	0.999120	1.017851	1.008905	1.130455	1.421736	1.114427	1.030183	1.002475
14	Portugal	PT	LT80	normalized	1.014427	1.003856	0.981716	0.995044	0.976160	1.077223	1.079219	1.056038	1.019550	1.000644
15	Czechia	CZ	LT80	normalized	1.016100	0.979352	1.004548	1.027520	1.023851	1.174844	1.324658	1.084517	1.020200	0.989842
16	Greece	EL	LT80	normalized	1.027115	0.983239	0.989646	0.954367	0.959678	1.028211	1.152957	1.074960	1.013372	0.989274
17	Denmark	DK	LT80	normalized	1.010938	0.999848	0.989214	1.020657	1.005069	1.023020	1.039904	1.069213	1.031748	0.983383
18	Switzerland	CH	LT80	normalized	1.035616	0.972509	0.991875	0.997235	0.997587	1.061751	1.058817	1.057173	1.011007	0.972313
19	Slovenia	SI	LT80	normalized	1.029287	0.970468	1.000245	1.000559	0.999616	1.096075	1.121134	1.044700	1.001922	0.966484
20	Estonia	EE	LT80	normalized	1.030864	0.996006	0.973130	0.977366	0.941782	0.967320	1.096466	0.995885	0.947470	0.960300
21	Montenegro	ME	LT80	normalized	1.014343	1.010269	0.975388	0.978189	0.983281	1.097853	1.373334	1.026564	0.951201	0.959582
22	Hungary	HU	LT80	normalized	1.022566	0.974640	1.002795	0.997790	0.984532	1.092329	1.230570	1.037761	0.975407	0.954401
23	Bulgaria	BG	LT80	normalized	1.019357	0.986378	0.994265	0.990131	0.985726	1.172561	1.436468	1.093518	0.945354	0.942555
24	Serbia	RS	LT80	normalized	1.037063	0.975378	0.987559	0.964466	0.969101	1.164052	1.333456	0.998404	0.644844	0.942320
25	Armenia	AM	LT80	normalized	1.035115	1.008866	0.956019	0.895082	0.866039	1.251799	1.237074	0.909807	0.872500	0.926923
26	Italy	IT	LT80	normalized	1.037174	0.970996	0.991830	0.964604	0.945012	1.095522	1.051458	1.004187	0.949085	0.916106
27	Germany	DE	LT80	normalized	1.028583	0.988456	0.982961	0.991662	0.958617	0.982483	0.998447	1.003770	0.977197	0.914716
28	Sweden	SE	LT80	normalized	1.024029	0.989863	0.986107	0.998720	0.958976	1.056766	1.005839	0.994011	0.979409	0.910600
29	Croatia	HR	LT80	normalized	1.053916	0.963024	0.983060	0.959672	0.932277	1.039490	1.131585	0.990455	0.927541	0.897778
30	Lithuania	LT	LT80	normalized	1.055938	1.008323	0.935740	0.921503	0.892768	1.045249	1.102589	0.960708	0.870034	0.879276
31	Latvia	LV	LT80	normalized	1.022160	0.991260	0.986580	0.970320	0.912900	0.951600	1.127640	0.966600	0.895140	0.833580
32	Romania	RO	LT80	normalized	1.024865	0.986505	0.988630	0.988669	0.976968	1.130885	1.277982	0.997127	0.911279	0.829676

TOTAL

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Malta	MT	TOTAL	normalized	1.016949	0.955701	1.027350	1.060863	1.064330	1.199538	1.196071	1.219183	1.161980	1.183648
1	Iceland	IS	TOTAL	normalized	0.988146	1.020151	0.991702	1.000148	1.005927	1.042377	1.039265	1.195733	1.140169	1.153060
2	Netherlands	NL	TOTAL	normalized	1.003373	0.993167	1.003460	1.024649	1.015107	1.147749	1.140424	1.133556	1.131250	1.143136
3	Cyprus	CY	TOTAL	normalized	1.029186	0.937156	1.033658	0.997362	1.069610	1.143922	1.241800	1.239908	1.150459	1.124312
4	Ireland	IE	TOTAL	normalized	1.007002	0.998439	0.994559	1.013608	1.002031	1.041412	1.098655	1.128063	1.125593	1.117736
5	Liechtenstein	LI	TOTAL	normalized	0.997423	1.043814	0.958763	1.055412	1.005155	1.256443	1.032216	1.078608	1.020619	1.117268
6	Luxembourg	LU	TOTAL	normalized	0.992076	0.966098	1.041827	1.053835	1.050895	1.143779	1.100400	1.089127	1.081529	1.095744
7	Germany	DE	TOTAL	normalized	1.016481	0.979311	1.004209	1.029001	1.012227	1.082065	1.101127	1.149954	1.106922	1.079127
8	Finland	FI	TOTAL	normalized	0.996378	1.003504	1.000118	1.016952	1.004682	1.051873	1.076956	1.155757	1.143393	1.078209
9	Norway	NO	TOTAL	normalized	1.016140	0.989495	0.994364	0.996615	0.991477	1.008311	1.026049	1.115550	1.065612	1.072463
10	Portugal	PT	TOTAL	normalized	1.006119	1.000327	0.993554	1.024547	1.013474	1.140328	1.128946	1.127418	1.071651	1.067896
11	Austria	AT	TOTAL	normalized	1.023795	0.972870	1.003336	1.012615	1.006371	1.125312	1.104853	1.130013	1.081778	1.067871
12	Denmark	DK	TOTAL	normalized	1.007454	0.991352	1.001194	1.038393	1.014429	1.045972	1.075440	1.117541	1.090769	1.064901
13	France	FR	TOTAL	normalized	1.007603	0.985137	1.007261	1.013398	1.019252	1.131754	1.099201	1.122302	1.061615	1.062469
14	Slovenia	SI	TOTAL	normalized	1.005897	0.975647	1.018456	1.018057	1.023988	1.216257	1.150672	1.118378	1.070087	1.061216
15	Spain	ES	TOTAL	normalized	1.023104	0.971158	1.005738	1.013880	0.992168	1.192895	1.070740	1.101128	1.039692	1.042102
16	Switzerland	CH	TOTAL	normalized	1.031079	0.969066	0.999855	1.002628	1.012027	1.137130	1.058255	1.110659	1.069182	1.037839
17	Estonia	EE	TOTAL	normalized	1.003717	0.992890	1.003393	1.016879	0.994057	1.041062	1.194009	1.115234	1.034320	1.030883
18	Greece	EL	TOTAL	normalized	1.012210	0.969465	1.018325	0.984534	1.022175	1.091374	1.179188	1.151479	1.047889	1.026689
19	Belgium	BE	TOTAL	normalized	1.024324	0.979883	0.995793	1.005480	0.988120	1.170333	1.020196	1.058305	1.012988	1.015740
20	Poland	PL	TOTAL	normalized	1.011934	0.974416	1.013649	1.035756	1.026469	1.222837	1.306178	1.125759	1.027167	1.015246
21	Czechia	CZ	TOTAL	normalized	1.024453	0.970711	1.004837	1.019293	1.014188	1.190285	1.259588	1.084875	1.016894	1.003669
22	Slovakia	SK	TOTAL	normalized	1.023092	0.973345	1.003563	1.011692	0.992593	1.126210	1.363524	1.109279	1.008254	0.995303
23	Italy	IT	TOTAL	normalized	1.028396	0.960289	1.011315	0.983592	0.989873	1.165225	1.087305	1.095037	1.013642	0.986208
24	Montenegro	ME	TOTAL	normalized	0.999019	0.994684	1.006297	1.003200	1.018684	1.138065	1.411355	1.088052	0.977652	0.974413
25	Sweden	SE	TOTAL	normalized	1.019326	0.985579	0.995095	0.995218	0.956506	1.079097	0.985779	1.011546	1.005714	0.969874
26	Hungary	HU	TOTAL	normalized	1.026484	0.967604	1.005912	1.000787	0.990451	1.097414	1.187944	1.041669	0.978737	0.968079
27	Croatia	HR	TOTAL	normalized	1.036057	0.963041	1.000902	0.987618	0.969749	1.088706	1.174273	1.066778	0.959584	0.946901
28	Serbia	RS	TOTAL	normalized	1.024670	0.974227	1.001103	0.980439	0.979254	1.155481	1.318381	1.013430	0.917367	0.925909
29	Armenia	AM	TOTAL	normalized	1.008985	1.007475	0.983540	0.920898	0.919641	1.278886	1.266846	0.964385	0.873495	0.916035
30	Lithuania	LT	TOTAL	normalized	1.034162	0.993484	0.972354	0.959272	0.928441	1.077686	1.154059	1.030467	0.919153	0.914776
31	Latvia	LV	TOTAL	normalized	1.009782	0.991345	0.998873	1.001801	0.963078	1.022469	1.201443	1.055301	0.962590	0.912575
32	Bulgaria	BG	TOTAL	normalized	1.024011	0.977541	0.998447	0.988264	0.984702	1.150005	1.357649	1.080035	0.919335	0.911709
33	Romania	RO	TOTAL	normalized	1.020584	0.981718	0.997698	1.005356	0.993947	1.151277	1.276275	1.026653	0.918109	0.837779

Y20-39

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Iceland	IS	Y20-39	normalized	0.794118	1.023529	1.182353	1.200000	0.988235	1.147059	1.129412	1.182353	1.323529	1.217647
1	Netherlands	NL	Y20-39	normalized	0.995277	1.007084	0.997639	1.011806	1.027745	1.087957	1.122786	1.131051	1.154073	1.190902
2	Austria	AT	Y20-39	normalized	1.048147	0.980678	0.971175	1.017738	1.014888	0.999683	1.100412	1.123218	1.074755	1.153627
3	Malta	MT	Y20-39	normalized	0.989189	1.021622	0.989189	1.151351	0.810811	1.151351	0.810811	1.054054	0.924324	1.054054
4	Montenegro	ME	Y20-39	normalized	0.941019	0.924933	1.134048	0.844504	1.005362	0.997319	1.174263	1.013405	0.997319	1.028150
5	Luxembourg	LU	Y20-39	normalized	0.994737	0.884211	1.121053	1.121053	1.168421	1.073684	0.978947	1.105263	0.978947	1.026316
6	Norway	NO	Y20-39	normalized	1.017909	1.020813	0.961278	1.020813	1.019361	1.010649	0.920620	0.962730	1.122459	1.010649
7	France	FR	Y20-39	normalized	1.035161	0.988817	0.976022	0.991828	0.991183	0.978710	0.997527	1.031290	1.004086	0.983548
8	Portugal	PT	Y20-39	normalized	1.053143	0.942003	1.004854	0.924374	0.954267	0.997956	0.948901	0.960399	0.958099	0.961165
9	Denmark	DK	Y20-39	normalized	0.959227	1.071888	0.968884	0.981760	0.976931	1.062232	0.939914	1.015558	0.984979	0.951180
10	Finland	FI	Y20-39	normalized	0.976999	1.001095	1.021906	0.975904	1.085433	1.059146	0.949617	0.986857	1.047097	0.950712
11	Belgium	BE	Y20-39	normalized	1.014289	1.005232	0.980479	0.951499	0.909237	0.927953	0.940028	0.906219	0.906219	0.913463
12	Switzerland	CH	Y20-39	normalized	1.009773	0.981626	1.008600	0.948788	0.921814	0.966380	0.983972	1.002737	0.971071	0.907740
13	Spain	ES	Y20-39	normalized	1.036045	0.980090	0.983865	0.958774	0.908371	0.995189	0.974761	0.960995	0.959218	0.906373
14	Armenia	AM	Y20-39	normalized	1.066728	1.026965	0.906307	0.957038	0.822669	2.520110	1.542505	1.026965	0.874771	0.892710
15	Slovakia	SK	Y20-39	normalized	1.059393	0.984413	0.956195	0.965869	0.981188	0.944907	1.099704	0.969901	0.902177	0.891696
16	Hungary	HU	Y20-39	normalized	1.072598	0.951419	0.975983	0.948144	0.885371	0.967795	1.144651	0.925218	0.896834	0.887009
17	Czechia	CZ	Y20-39	normalized	1.060503	0.984560	0.954937	0.979713	0.936625	0.945781	0.997487	0.960323	0.938241	0.877917
18	Poland	PL	Y20-39	normalized	1.021684	0.990499	0.987817	0.996060	0.985632	1.040158	1.098656	1.035390	0.911342	0.844402
19	Italy	IT	Y20-39	normalized	1.063570	0.990165	0.946265	0.953729	0.892944	0.881569	0.924048	0.883346	0.865395	0.834647
20	Cyprus	CY	Y20-39	normalized	1.161290	0.832258	1.006452	1.006452	1.209677	1.122581	1.335484	0.929032	1.248387	0.832258
21	Serbia	RS	Y20-39	normalized	1.083099	0.952113	0.964789	0.888732	0.884507	0.990141	1.138732	0.957042	0.571831	0.795070
22	Estonia	EE	Y20-39	normalized	1.039134	1.099084	0.861782	0.756869	0.709409	0.741882	0.809326	0.851790	0.824313	0.794338
23	Slovenia	SI	Y20-39	normalized	1.060811	1.037162	0.902027	0.875000	0.945946	0.837838	0.820946	0.956081	0.810811	0.793919
24	Sweden	SE	Y20-39	normalized	1.054507	0.955597	0.989896	1.005850	0.901356	0.914119	0.883807	0.861473	0.906142	0.767349
25	Bulgaria	BG	Y20-39	normalized	1.060354	1.010520	0.929125	0.954042	0.934662	0.932447	1.182724	0.883167	0.801772	0.761351
26	Croatia	HR	Y20-39	normalized	1.107878	0.945309	0.946814	0.925740	0.939288	0.918214	0.955845	0.886603	0.812845	0.757150
27	Greece	EL	Y20-39	normalized	1.070435	1.006182	0.923383	0.937955	0.892250	0.863767	0.948554	0.881652	0.835284	0.716715
28	Romania	RO	Y20-39	normalized	1.072698	0.969709	0.957593	0.903877	0.897819	0.926898	0.991922	0.782512	0.751010	0.688813
29	Lithuania	LT	Y20-39	normalized	1.135041	0.986612	0.878347	0.880093	0.798021	0.789290	0.850407	0.729045	0.709837	0.679278
30	Latvia	LV	Y20-39	normalized	1.052184	0.978155	0.969660	0.867718	0.774272	0.743932	0.902913	0.870146	0.794903	0.675971
31	Liechtenstein	LI	Y20-39	normalized	0.600000	1.500000	0.900000	1.800000	0.900000	0.900000	0.900000	0.000000	1.500000	0.600000
32	Germany	DE	Y20-39	normalized	1.031710	0.986478	0.981813	0.984855	0.974004	0.997836	0.997025	1.033738	0.992056	0.000000

Y40-59

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Liechtenstein	LI	Y40-59	normalized	1.094595	0.851351	1.054054	0.891892	0.770270	0.648649	0.972973	0.000000	1.094595	1.013514
1	Iceland	IS	Y40-59	normalized	1.093407	0.912088	0.994505	1.005495	1.115385	1.104396	1.060440	1.082418	1.258242	1.010989
2	Czechia	CZ	Y40-59	normalized	1.035208	0.988029	0.976763	0.976059	0.972840	1.077859	1.230560	1.035711	1.001811	0.970526
3	Norway	NO	Y40-59	normalized	1.036278	1.006382	0.957340	0.962042	0.921061	0.942224	0.926772	0.994290	0.956332	0.936177
4	Estonia	EE	Y40-59	normalized	1.048814	1.014427	0.936759	0.974704	0.935573	0.992490	1.022134	0.972332	0.937352	0.931423
5	Spain	ES	Y40-59	normalized	1.026911	0.987325	0.985764	0.983054	0.966589	1.053125	1.010711	0.996308	0.956045	0.929124
6	Greece	EL	Y40-59	normalized	1.021378	0.994886	0.983737	0.964641	0.969056	1.012216	1.144345	1.012326	0.951505	0.909780
7	Luxembourg	LU	Y40-59	normalized	0.984219	1.026578	0.989203	1.034053	0.996678	1.044020	1.041528	0.959302	0.941860	0.904485
8	Cyprus	CY	Y40-59	normalized	1.015748	0.981389	1.002863	0.914817	0.970651	1.157480	1.262706	1.039370	1.088762	0.901933
9	Netherlands	NL	Y40-59	normalized	1.026651	1.007007	0.966341	0.952298	0.920678	0.955055	0.989259	0.958329	0.903532	0.887899
10	Austria	AT	Y40-59	normalized	1.038582	0.988316	0.973102	1.000099	0.942972	0.985184	0.995774	0.954905	0.905534	0.887784
11	Bulgaria	BG	Y40-59	normalized	1.019183	0.995337	0.985480	0.973716	0.942875	1.098431	1.304859	0.983334	0.875550	0.886678
12	Portugal	PT	Y40-59	normalized	1.004711	0.997906	0.997383	0.979483	0.943473	1.018005	0.987753	0.956768	0.915001	0.885690
13	Italy	IT	Y40-59	normalized	1.036317	0.981260	0.982423	0.974773	0.953359	1.042061	1.032480	0.973140	0.925236	0.874882
14	France	FR	Y40-59	normalized	1.042235	0.987464	0.970301	0.962393	0.928553	0.963311	0.949071	0.938990	0.893382	0.873726
15	Montenegro	ME	Y40-59	normalized	1.082461	0.986911	0.930628	1.014398	1.048429	1.018325	1.267016	0.920157	0.829843	0.856457
16	Malta	MT	Y40-59	normalized	0.974359	1.043956	0.981685	1.025641	0.941392	1.106227	1.084249	1.080586	0.970696	0.846154
17	Germany	DE	Y40-59	normalized	1.033932	0.998634	0.967435	0.976447	0.935175	0.955307	0.972568	0.933598	0.885293	0.838836
18	Slovakia	SK	Y40-59	normalized	1.066639	0.965570	0.967791	0.976260	0.922810	0.972928	1.208802	0.965848	0.877690	0.838817
19	Serbia	RS	Y40-59	normalized	1.058988	0.973158	0.967854	0.926868	0.922528	1.101517	1.196509	0.890510	0.569275	0.835059
20	Belgium	BE	Y40-59	normalized	1.061509	0.987417	0.951074	0.937338	0.899697	0.965460	0.920681	0.901103	0.854269	0.834151
21	Finland	FI	Y40-59	normalized	1.042336	1.002190	0.955474	0.953041	0.889538	0.943552	0.910219	0.900973	0.875912	0.827494
22	Hungary	HU	Y40-59	normalized	1.072314	0.976552	0.951134	0.931626	0.895024	0.950181	1.122450	0.904620	0.824236	0.820995
23	Switzerland	CH	Y40-59	normalized	1.035309	0.975200	0.989491	0.974989	0.914880	0.922657	0.942833	0.934636	0.876629	0.817781
24	Latvia	LV	Y40-59	normalized	1.039009	0.992923	0.968068	0.981013	0.898680	0.906447	1.115647	0.944766	0.859066	0.813498
25	Romania	RO	Y40-59	normalized	1.028697	0.997176	0.974128	0.976735	0.971106	1.053908	1.180850	0.948532	0.886615	0.806273
26	Sweden	SE	Y40-59	normalized	1.059663	0.985254	0.955083	0.929397	0.890459	0.941628	0.913699	0.876597	0.868443	0.800761
27	Poland	PL	Y40-59	normalized	1.051089	0.987253	0.961658	0.940221	0.895069	0.957177	1.046741	0.894082	0.811068	0.792992
28	Croatia	HR	Y40-59	normalized	1.084327	0.956285	0.959388	0.958147	0.905399	0.924843	1.029511	0.866510	0.841274	0.782941
29	Slovenia	SI	Y40-59	normalized	1.060018	0.975601	0.964381	0.913624	0.875156	0.959038	0.970793	0.875690	0.855387	0.778451
30	Denmark	DK	Y40-59	normalized	1.042183	1.006376	0.951442	0.957002	0.890503	0.878494	0.875380	0.862481	0.824672	0.777522
31	Lithuania	LT	Y40-59	normalized	1.063919	1.029615	0.906466	0.860671	0.836659	0.985707	1.007490	0.845235	0.789320	0.775256
32	Armenia	AM	Y40-59	normalized	1.065157	1.011936	0.922907	0.891024	0.845405	1.067119	1.026161	0.775262	0.707325	0.713129

Y60-79

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Iceland	IS	Y60-79	normalized	0.936424	1.078394	0.985182	1.013862	1.006692	1.104207	1.135755	1.346558	1.197419	1.300669
1	Malta	MT	Y60-79	normalized	1.027934	0.945669	1.026397	1.068683	1.065607	1.194003	1.255510	1.257048	1.183239	1.253203
2	France	FR	Y60-79	normalized	1.001712	0.990021	1.008267	1.022124	1.029084	1.147847	1.175806	1.192257	1.156656	1.161545
3	Netherlands	NL	Y60-79	normalized	1.006386	0.996193	0.997421	1.028461	1.022510	1.152640	1.182317	1.161681	1.160779	1.160364
4	Norway	NO	Y60-79	normalized	0.996253	0.995265	1.008483	1.026410	1.037576	1.058010	1.088243	1.184716	1.142101	1.152432
5	Liechtenstein	LI	Y60-79	normalized	0.822742	1.153846	1.023411	1.023411	1.013378	1.193980	0.983278	0.000000	0.872910	1.143813
6	Poland	PL	Y60-79	normalized	1.006538	0.979267	1.014195	1.052696	1.062426	1.268973	1.415998	1.196846	1.120739	1.118838
7	Spain	ES	Y60-79	normalized	1.030616	0.982768	0.986616	1.004716	1.007285	1.209219	1.147454	1.157316	1.126992	1.117345
8	Cyprus	CY	Y60-79	normalized	1.023144	0.956687	1.020169	1.042486	1.065796	1.142172	1.285998	1.265664	1.143660	1.115391
9	Finland	FI	Y60-79	normalized	0.993128	1.000244	1.006628	1.008721	1.004221	1.043101	1.073660	1.138337	1.151367	1.104900
10	Belgium	BE	Y60-79	normalized	1.021779	0.986703	0.991518	0.997160	0.983882	1.137149	1.080190	1.089907	1.057168	1.066001
11	Slovakia	SK	Y60-79	normalized	1.008638	0.979238	1.012124	1.033819	1.037392	1.191537	1.516013	1.171823	1.086851	1.062229
12	Portugal	PT	Y60-79	normalized	1.015604	1.007187	0.977209	1.000874	0.986145	1.099310	1.112425	1.088357	1.051461	1.035291
13	Luxembourg	LU	Y60-79	normalized	0.977408	0.925327	1.097265	1.021641	0.999524	1.081570	1.071581	1.036623	1.049465	1.034483
14	Austria	AT	Y60-79	normalized	1.010726	0.987562	1.001712	1.026982	1.016490	1.110993	1.098728	1.089344	1.048483	1.032006
15	Denmark	DK	Y60-79	normalized	1.005923	0.997159	0.996918	1.035054	1.032309	1.054844	1.079883	1.118500	1.080508	1.027061
16	Slovenia	SI	Y60-79	normalized	1.020434	0.964492	1.015074	1.028681	1.034592	1.145652	1.174792	1.093833	1.049024	1.023871
17	Greece	EL	Y60-79	normalized	1.027313	0.977934	0.994753	0.953931	0.961493	1.043055	1.169151	1.103654	1.039826	1.023034
18	Armenia	AM	Y60-79	normalized	1.022296	1.003221	0.974483	0.898186	0.884368	1.199729	1.282214	0.946677	0.933198	1.017760
19	Switzerland	CH	Y60-79	normalized	1.035228	0.972487	0.992286	1.004731	1.023708	1.102237	1.093854	1.090666	1.048650	1.017639
20	Czechia	CZ	Y60-79	normalized	1.010894	0.976813	1.012294	1.040273	1.037745	1.207502	1.362255	1.101247	1.029428	0.999694
21	Hungary	HU	Y60-79	normalized	1.007109	0.974702	1.018189	1.019195	1.012427	1.137502	1.266804	1.079099	1.020917	0.993624
22	Montenegro	ME	Y60-79	normalized	1.001448	1.017815	0.980737	0.977063	0.965037	1.124374	1.418662	1.047545	0.980069	0.985024
23	Estonia	EE	Y60-79	normalized	1.024266	0.984170	0.991564	0.994029	0.960999	0.979240	1.138475	1.014078	0.958863	0.981047
24	Germany	DE	Y60-79	normalized	1.027855	0.985397	0.986748	0.995439	0.963581	0.989393	1.005570	1.019991	0.998744	0.972759
25	Serbia	RS	Y60-79	normalized	1.030788	0.976346	0.992867	0.975233	0.982402	1.186175	1.375226	1.026807	0.664629	0.971338
26	Bulgaria	BG	Y60-79	normalized	1.017113	0.983431	0.999455	0.996418	1.000251	1.207767	1.489684	1.136026	0.972162	0.968141
27	Lithuania	LT	Y60-79	normalized	1.044912	1.002053	0.953035	0.948736	0.923008	1.092519	1.164314	1.027974	0.916271	0.939625
28	Sweden	SE	Y60-79	normalized	1.016499	0.992130	0.991371	1.010873	0.974664	1.083152	1.029478	1.020710	1.003072	0.937179
29	Croatia	HR	Y60-79	normalized	1.045235	0.965003	0.989761	0.961170	0.938629	1.070594	1.163621	1.023295	0.951701	0.930869
30	Italy	IT	Y60-79	normalized	1.036313	0.968224	0.995463	0.962589	0.946423	1.117576	1.063062	1.018156	0.959284	0.929829
31	Latvia	LV	Y60-79	normalized	1.014798	0.991500	0.993702	0.975826	0.928212	0.982858	1.155182	0.985145	0.917876	0.855860
32	Romania	RO	Y60-79	normalized	1.020654	0.984388	0.994957	0.998664	0.985260	1.171096	1.331617	1.027356	0.931207	0.848400

Y_GE80

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Malta	MT	Y_GE80	normalized	1.016626	0.944891	1.038483	1.063703	1.090043	1.230712	1.195404	1.230151	1.197646	1.202690
1	Germany	DE	Y_GE80	normalized	1.006407	0.971699	1.021894	1.060079	1.056850	1.164951	1.186592	1.271628	1.214897	1.187499
2	Netherlands	NL	Y_GE80	normalized	0.999626	0.988800	1.011575	1.032805	1.023210	1.173788	1.137483	1.141598	1.145123	1.168635
3	Cyprus	CY	Y_GE80	normalized	1.031191	0.919405	1.049403	0.978438	1.083316	1.147059	1.209546	1.261356	1.156165	1.168411
4	Slovenia	SI	Y_GE80	normalized	0.984707	0.980338	1.034954	1.033910	1.046068	1.325133	1.177432	1.185125	1.131839	1.147036
5	Luxembourg	LU	Y_GE80	normalized	1.002755	0.983930	1.013315	1.076217	1.089532	1.202020	1.134068	1.145546	1.129936	1.146924
6	Denmark	DK	Y_GE80	normalized	1.004042	0.983036	1.012922	1.055755	1.023407	1.068441	1.110229	1.164853	1.148548	1.144705
7	Liechtenstein	LI	Y_GE80	normalized	1.119171	0.979275	0.901554	1.103627	1.064767	1.398964	1.088083	0.000000	1.088083	1.142487
8	Portugal	PT	Y_GE80	normalized	1.000130	0.997815	1.002055	1.045915	1.040470	1.185789	1.164868	1.178841	1.109225	1.116408
9	Austria	AT	Y_GE80	normalized	1.029105	0.961256	1.009639	1.007036	1.009617	1.158656	1.126897	1.182519	1.128778	1.114539
10	Estonia	EE	Y_GE80	normalized	0.972399	0.989295	1.038306	1.062462	1.054364	1.126135	1.306539	1.252921	1.134512	1.112311
11	Finland	FI	Y_GE80	normalized	0.993130	1.006171	1.000700	1.032529	1.018627	1.073476	1.107473	1.210323	1.179802	1.101244
12	Iceland	IS	Y_GE80	normalized	1.006457	1.008006	0.985537	0.985537	1.001033	0.995610	0.977014	1.138171	1.087035	1.092459
13	Switzerland	CH	Y_GE80	normalized	1.028260	0.966928	1.004811	1.005978	1.020995	1.183948	1.057906	1.143878	1.105314	1.078536
14	Greece	EL	Y_GE80	normalized	1.002392	0.960390	1.037218	1.004366	1.063222	1.132983	1.196455	1.201615	1.070628	1.051214
15	Norway	NO	Y_GE80	normalized	1.025010	0.983224	0.991766	0.984739	0.974092	0.989915	1.008556	1.099452	1.035530	1.048029
16	France	FR	Y_GE80	normalized	1.004114	0.982417	1.013469	1.018071	1.018234	1.142014	1.078183	1.110447	1.036079	1.038563
17	Italy	IT	Y_GE80	normalized	1.023231	0.953990	1.022779	0.994764	1.016269	1.206237	1.108397	1.148491	1.051627	1.027454
18	Spain	ES	Y_GE80	normalized	1.019018	0.963549	1.017433	1.023420	0.990805	1.194317	1.047491	1.093311	1.013904	1.026137
19	Belgium	BE	Y_GE80	normalized	1.020706	0.974733	1.004561	1.021500	1.005688	1.228524	1.004942	1.069950	1.018007	1.020770
20	Czechia	CZ	Y_GE80	normalized	1.034472	0.960345	1.005183	1.009424	1.002595	1.208808	1.181531	1.085303	1.012929	1.020256
21	Latvia	LV	Y_GE80	normalized	0.992681	0.991351	1.015968	1.045492	1.032685	1.120758	1.303560	1.175815	1.052146	1.019128
22	Sweden	SE	Y_GE80	normalized	1.016228	0.982756	1.001015	0.992911	0.954879	1.093806	0.972565	1.023096	1.023041	1.008917
23	Poland	PL	Y_GE80	normalized	1.004048	0.964785	1.031168	1.052632	1.037325	1.278953	1.301091	1.139304	1.015423	1.000104
24	Croatia	HR	Y_GE80	normalized	1.017100	0.963122	1.019778	1.017294	1.009650	1.141163	1.219822	1.148148	0.993778	0.999289
25	Montenegro	ME	Y_GE80	normalized	0.975237	0.970495	1.054268	1.042018	1.073630	1.200474	1.470364	1.183483	1.018704	0.997432
26	Hungary	HU	Y_GE80	normalized	1.032011	0.957518	1.010471	1.005189	0.998880	1.104543	1.126661	1.047260	0.983313	0.987980
27	Slovakia	SK	Y_GE80	normalized	1.021014	0.968713	1.010273	1.002392	0.967963	1.119799	1.275623	1.101506	0.975140	0.984474
28	Lithuania	LT	Y_GE80	normalized	1.006995	0.974970	1.018035	1.006394	0.972948	1.118155	1.218275	1.117499	0.980435	0.959067
29	Serbia	RS	Y_GE80	normalized	1.008114	0.972719	1.019168	1.001801	0.992752	1.143336	1.297839	1.033937	0.630527	0.904344
30	Armenia	AM	Y_GE80	normalized	0.966802	1.005230	1.027967	0.962574	1.006170	1.322612	1.314908	1.052490	0.875102	0.898458
31	Bulgaria	BG	Y_GE80	normalized	1.030293	0.965615	1.004092	0.985744	0.983319	1.119563	1.251279	1.061838	0.884221	0.870080
32	Romania	RO	Y_GE80	normalized	1.014486	0.974899	1.010615	1.029126	1.018134	1.180326	1.273844	1.068713	0.927839	0.849322

Y_LT20

index	name	abbr	filter	value_type	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	Luxembourg	LU	Y_LT20	normalized	1.000000	0.944444	1.055556	1.055556	1.111111	1.277778	1.055556	1.111111	1.138889	2.638889
1	Cyprus	CY	Y_LT20	normalized	0.992308	1.107692	0.900000	1.153846	0.969231	0.900000	1.107692	1.361538	1.476923	1.384615
2	Denmark	DK	Y_LT20	normalized	0.999054	0.947966	1.052980	1.052980	0.911069	0.922422	0.959319	0.896878	0.882687	1.092715
3	Iceland	IS	Y_LT20	normalized	1.173913	0.782609	1.043478	0.869565	0.434783	1.043478	1.217391	0.782609	1.000000	1.043478
4	Norway	NO	Y_LT20	normalized	0.979592	0.993998	1.026411	0.911164	0.979592	0.929172	0.900360	0.893157	1.044418	0.986795
5	Netherlands	NL	Y_LT20	normalized	0.966754	0.980579	1.052666	0.978604	0.999342	1.036866	0.950955	1.004279	1.012179	0.978786
6	Austria	AT	Y_LT20	normalized	0.995976	1.016097	0.987928	0.901408	0.993964	0.993964	0.891348	0.891348	0.963783	0.975855
7	Finland	FI	Y_LT20	normalized	0.941687	0.975186	1.083127	1.034739	1.016129	0.971464	0.993797	0.964020	1.057072	0.956576
8	Spain	ES	Y_LT20	normalized	1.025691	0.994183	0.980126	0.950557	0.915657	0.854581	0.881726	0.951527	0.912748	0.949103
9	France	FR	Y_LT20	normalized	1.039097	0.983797	0.977106	0.963527	0.962149	0.899764	0.909210	0.981829	0.935778	0.944831
10	Portugal	PT	Y_LT20	normalized	1.017308	1.046154	0.936538	1.069231	0.965385	0.890385	0.871154	0.963462	0.965385	0.900000
11	Serbia	RS	Y_LT20	normalized	1.037875	0.993782	0.968344	0.954777	0.937818	0.929339	0.900509	0.744488	0.581685	0.883550
12	Czechia	CZ	Y_LT20	normalized	0.976979	1.022459	1.000561	1.010668	1.030882	0.882650	0.892757	0.941606	0.840539	0.869175
13	Slovakia	SK	Y_LT20	normalized	1.064392	0.981326	0.954282	1.004507	0.994849	1.025757	0.925306	0.952350	0.915647	0.863490
14	Hungary	HU	Y_LT20	normalized	1.068474	0.987161	0.944365	0.821683	0.918688	0.828816	0.850214	0.861626	0.766049	0.843081
15	Estonia	EE	Y_LT20	normalized	1.104089	0.992565	0.903346	0.881041	0.791822	0.691450	0.925651	0.847584	0.914498	0.836431
16	Montenegro	ME	Y_LT20	normalized	0.919708	1.138686	0.941606	0.810219	1.029197	0.963504	0.722628	1.467153	0.963504	0.830292
17	Sweden	SE	Y_LT20	normalized	1.033414	0.989672	0.976914	0.960510	0.874848	1.018834	0.860267	0.936817	0.889429	0.816525
18	Slovenia	SI	Y_LT20	normalized	1.003521	1.119718	0.876761	0.950704	0.940141	0.802817	0.908451	0.887324	0.876761	0.813380
19	Greece	EL	Y_LT20	normalized	0.995420	1.070229	0.934351	0.874809	0.882443	0.786260	0.824427	0.758779	0.778626	0.801527
20	Switzerland	CH	Y_LT20	normalized	1.097222	0.932540	0.970238	1.000000	0.898810	0.974206	0.928571	1.013889	0.894841	0.791667
21	Poland	PL	Y_LT20	normalized	1.029240	0.976952	0.993808	0.976264	0.911249	0.826281	0.898865	0.883385	0.810802	0.740282
22	Italy	IT	Y_LT20	normalized	1.055676	0.974331	0.969993	0.977946	0.831164	0.781996	0.783080	0.734635	0.760304	0.732104
23	Belgium	BE	Y_LT20	normalized	1.011116	0.944420	1.044464	1.008448	0.975100	0.896398	0.897732	0.875056	0.716318	0.705647
24	Bulgaria	BG	Y_LT20	normalized	1.065617	0.965879	0.968504	0.952756	0.904199	0.759843	0.876640	0.748031	0.758530	0.691601
25	Croatia	HR	Y_LT20	normalized	1.070718	0.967956	0.961326	0.951381	0.891713	0.911602	0.855249	0.848619	0.828729	0.672928
26	Armenia	AM	Y_LT20	normalized	1.044905	1.079447	0.875648	0.782383	0.692573	2.015544	1.417962	0.958549	0.796200	0.624928
27	Malta	MT	Y_LT20	normalized	1.008000	1.056000	0.936000	0.792000	1.104000	0.720000	0.672000	0.720000	0.576000	0.600000
28	Romania	RO	Y_LT20	normalized	1.065895	0.971632	0.962473	0.873553	0.846076	0.781580	0.767078	0.730823	0.676250	0.593436
29	Latvia	LV	Y_LT20	normalized	1.005650	1.000000	0.994350	0.847458	0.847458	0.819209	0.598870	0.655367	0.632768	0.519774
30	Lithuania	LT	Y_LT20	normalized	1.185786	1.002494	0.811721	0.837905	0.759352	0.684539	0.658354	0.549875	0.620948	0.486284
31	Liechtenstein	LI	Y_LT20	normalized	1.285714	1.285714	0.428571	0.428571	0.000000	3.000000	0.857143	0.000000	2.142857	0.428571
32	Germany	DE	Y_LT20	normalized	0.983753	1.040361	0.975885	0.992277	0.965176	0.912502	0.927364	0.960367	0.951188	0.000000

Post HeatMaps Notes

• More Deaths on the larger age groups

  • European countries have strongly recommended COVID-19 vaccination for older adults due to their increased risk of severe illness.
  • The European Centre for Disease Prevention and Control (ECDC) has emphasized the importance of vaccinating high-risk groups, including the elderly.
    • ecdc.europa.eu

• Iceland and Netherlands Y20-39 agegroup

  • dispite being young this agegroup had a higher than usual death rate in 2022,2023,2024.
    • Possible reasons:
      • colder-climate?
        • but Finland, and Sweden are futher north than netherlands
      • vaccine hesitancy? - we will see the vaccination rate of all countries later in this document
      • Young people feeling compelled to be vaccinated due to needing a Covid/Corona Pass (or other name)
        • but many countries had these passes (see table below)
          • How were they enforced (in each country) ? subjective
          • Do contries have a nightlife or culture that compelled people?

Country	Name	Used For	Active Period	Notes
🇫🇷 France	Pass Sanitaire / Passe Vaccinal	Restaurants, cafés, cinemas, museums, transport, hospitals	July 2021 – March 2022
🇮🇹 Italy	Green Pass / Super Green Pass	Workplaces, restaurants, domestic travel, gyms, theaters, stadiums	June 2021 – April 2022
🇦🇹 Austria	3G/2G Rule	Restaurants, hotels, salons, gyms, public transport	Mid-2021 – March 2022
🇩🇪 Germany	3G/2G Rule	Restaurants, events, shops, workplaces, inter-regional travel	2021 – March 2022	Varied by region
🇨🇭 Switzerland	COVID Certificate	Indoor dining, large events, cinemas, nightlife	Sept 2021 – Feb 2022
🇳🇱 Netherlands	CoronaToegangsbewijs	Restaurants, bars, events, festivals, sports	Sept 2021 – March 2022	Tests always accepted as alternative
🇧🇪 Belgium	Covid Safe Ticket	Hospitality venues, events, cultural spaces, gyms	July 2021 – March 2022
🇸🇮 Slovenia	PCT Certificate	Public services, work, shops, cafes, public transport	2021 – Feb 2022
🇱🇹 Lithuania	Opportunity Pass	Supermarkets, shops, gyms, restaurants, some healthcare	Mid-2021 – Feb 2022	One of the most strict implementations
🇱🇻 Latvia	COVID-19 Certificate	Workplaces, shops, services, restaurants, events	2021 – Feb 2022
🇱🇺 Luxembourg	CovidCheck	Workplaces, bars, restaurants, events	2021 – Feb 2022	Optional per employer
🇨🇿 Czech Republic	O-N-T System	Restaurants, hotels, cultural events	2021 – Feb 2022
🇵🇱 Poland	EU COVID Certificate	Recommended for travel, limited venue use	—	No widespread domestic mandate
🇵🇹 Portugal	Digital COVID Certificate	Hotels, restaurants (high-risk), events, air travel	Mid-2021 – Early 2022
🇪🇸 Spain	EU Digital COVID Certificate	Regional use, nightlife, restaurants, hospitals	—	Highly decentralized use
🇮🇸 Iceland	COVID-19 Certificate	International travel, some large events	—	Never broadly required, all restrictions lifted Feb 2022
🇩🇰 Denmark	Coronapas	Restaurants, gyms, salons	April 2021 – Sept 2021	First to implement and one of the first to end it
🇸🇪 Sweden	COVID Certificate	Only large indoor events (>100 people)	—	Never used for shops or restaurants, mostly optional
🇫🇮 Finland	COVID Passport	Events, hospitality	—	Regional use only, phased out early 2022

display(MD('### HeatMap (of deaths and vaccines)'))

temp = cd[(cd['source']=='deaths') & (cd['filter']=='TOTAL') & (cd['value_type']=='normalized')]

temp = pd.pivot_table(
    data = temp,
    values = 'value',
    index = ['abbr','name','filter','value_type'],
    columns=['year'],
    aggfunc='mean',
)
temp.columns.name = 'index' 
temp = temp.reset_index()

temp = temp.set_index('name').reindex(sppd['name']).reset_index()
temp['average(2022-2024)'] = (temp[2022]+temp[2023]+ temp[2024])/3.0
temp = temp.sort_values(by='average(2022-2024)',ascending=False)

## vaccines in 2023
v2023 = cd[(cd['source']=='vaccine') & (cd['filter']=='All') & (cd['value_type']=='ratio (total_dose/pop.)') & (cd['year'] == 2023)]
v2023 = v2023[['name','source','filter','value_type','value']]

temp = pd.merge(temp,v2023, how='left',on='name')

years = [y for y in range(2015,2025,1)]

styled_df = temp.style.background_gradient(cmap=heatmapCM1, axis=1, subset=years) \
                      .background_gradient(cmap=heatmapCM2, axis=0, subset=['average(2022-2024)']) \
                      .background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])

display(MD('* 2015-2024 are the deaths for those years'))
display(MD('* average(2022-2024) is how the table is sorted'))
display(MD('* value is the total_doses/pop. for 2023'))

display(styled_df)

display(MD('### Lets sort by the vaccines '))

temp = temp.sort_values(by='value',ascending=False)

styled_df = temp.style.background_gradient(cmap=heatmapCM1, axis=1, subset=years) \
                      .background_gradient(cmap=heatmapCM2, axis=0, subset=['average(2022-2024)']) \
                      .background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])

display(styled_df)


dxv = temp.copy()

HeatMap (of deaths and vaccines)

• 2015-2024 are the deaths for those years

• average(2022-2024) is how the table is sorted

• value is the total_doses/pop. for 2023

	name	abbr	filter_x	value_type_x	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	average(2022-2024)	source	filter_y	value_type_y	value
0	Malta	MT	TOTAL	normalized	1.016949	0.955701	1.027350	1.060863	1.064330	1.199538	1.196071	1.219183	1.161980	1.183648	1.188270	vaccine	All	ratio (total_dose/pop.)	2.322112
1	Cyprus	CY	TOTAL	normalized	1.029186	0.937156	1.033658	0.997362	1.069610	1.143922	1.241800	1.239908	1.150459	1.124312	1.171560	vaccine	All	ratio (total_dose/pop.)	1.988003
2	Iceland	IS	TOTAL	normalized	0.988146	1.020151	0.991702	1.000148	1.005927	1.042377	1.039265	1.195733	1.140169	1.153060	1.162987	vaccine	All	ratio (total_dose/pop.)	2.138589
3	Netherlands	NL	TOTAL	normalized	1.003373	0.993167	1.003460	1.024649	1.015107	1.147749	1.140424	1.133556	1.131250	1.143136	1.135981	vaccine	All	ratio (total_dose/pop.)	2.195485
4	Finland	FI	TOTAL	normalized	0.996378	1.003504	1.000118	1.016952	1.004682	1.051873	1.076956	1.155757	1.143393	1.078209	1.125786	vaccine	All	ratio (total_dose/pop.)	6.993552
5	Germany	DE	TOTAL	normalized	1.016481	0.979311	1.004209	1.029001	1.012227	1.082065	1.101127	1.149954	1.106922	1.079127	1.112001	vaccine	All	ratio (total_dose/pop.)	2.211555
6	Austria	AT	TOTAL	normalized	1.023795	0.972870	1.003336	1.012615	1.006371	1.125312	1.104853	1.130013	1.081778	1.067871	1.093221	vaccine	All	ratio (total_dose/pop.)	2.141405
7	Denmark	DK	TOTAL	normalized	1.007454	0.991352	1.001194	1.038393	1.014429	1.045972	1.075440	1.117541	1.090769	1.064901	1.091070	vaccine	All	ratio (total_dose/pop.)	2.402740
8	Portugal	PT	TOTAL	normalized	1.006119	1.000327	0.993554	1.024547	1.013474	1.140328	1.128946	1.127418	1.071651	1.067896	1.088988	vaccine	All	ratio (total_dose/pop.)	5.294853
9	Luxembourg	LU	TOTAL	normalized	0.992076	0.966098	1.041827	1.053835	1.050895	1.143779	1.100400	1.089127	1.081529	1.095744	1.088800	vaccine	All	ratio (total_dose/pop.)	1.914499
10	Norway	NO	TOTAL	normalized	1.016140	0.989495	0.994364	0.996615	0.991477	1.008311	1.026049	1.115550	1.065612	1.072463	1.084542	vaccine	All	ratio (total_dose/pop.)	2.212603
11	Slovenia	SI	TOTAL	normalized	1.005897	0.975647	1.018456	1.018057	1.023988	1.216257	1.150672	1.118378	1.070087	1.061216	1.083227	vaccine	All	ratio (total_dose/pop.)	1.379628
12	France	FR	TOTAL	normalized	1.007603	0.985137	1.007261	1.013398	1.019252	1.131754	1.099201	1.122302	1.061615	1.062469	1.082129	vaccine	All	ratio (total_dose/pop.)	4.584656
13	Greece	EL	TOTAL	normalized	1.012210	0.969465	1.018325	0.984534	1.022175	1.091374	1.179188	1.151479	1.047889	1.026689	1.075352	vaccine	All	ratio (total_dose/pop.)	2.032484
14	Switzerland	CH	TOTAL	normalized	1.031079	0.969066	0.999855	1.002628	1.012027	1.137130	1.058255	1.110659	1.069182	1.037839	1.072560	nan	nan	nan	nan
15	Liechtenstein	LI	TOTAL	normalized	0.997423	1.043814	0.958763	1.055412	1.005155	1.256443	1.032216	1.078608	1.020619	1.117268	1.072165	vaccine	All	ratio (total_dose/pop.)	1.815763
16	Spain	ES	TOTAL	normalized	1.023104	0.971158	1.005738	1.013880	0.992168	1.192895	1.070740	1.101128	1.039692	1.042102	1.060974	vaccine	All	ratio (total_dose/pop.)	2.196480
17	Estonia	EE	TOTAL	normalized	1.003717	0.992890	1.003393	1.016879	0.994057	1.041062	1.194009	1.115234	1.034320	1.030883	1.060146	vaccine	All	ratio (total_dose/pop.)	1.582880
18	Poland	PL	TOTAL	normalized	1.011934	0.974416	1.013649	1.035756	1.026469	1.222837	1.306178	1.125759	1.027167	1.015246	1.056057	vaccine	All	ratio (total_dose/pop.)	2.862610
19	Slovakia	SK	TOTAL	normalized	1.023092	0.973345	1.003563	1.011692	0.992593	1.126210	1.363524	1.109279	1.008254	0.995303	1.037612	vaccine	All	ratio (total_dose/pop.)	1.279721
20	Czechia	CZ	TOTAL	normalized	1.024453	0.970711	1.004837	1.019293	1.014188	1.190285	1.259588	1.084875	1.016894	1.003669	1.035146	vaccine	All	ratio (total_dose/pop.)	1.685242
21	Italy	IT	TOTAL	normalized	1.028396	0.960289	1.011315	0.983592	0.989873	1.165225	1.087305	1.095037	1.013642	0.986208	1.031629	vaccine	All	ratio (total_dose/pop.)	4.765223
22	Belgium	BE	TOTAL	normalized	1.024324	0.979883	0.995793	1.005480	0.988120	1.170333	1.020196	1.058305	1.012988	1.015740	1.029011	vaccine	All	ratio (total_dose/pop.)	2.411964
23	Montenegro	ME	TOTAL	normalized	0.999019	0.994684	1.006297	1.003200	1.018684	1.138065	1.411355	1.088052	0.977652	0.974413	1.013372	nan	nan	nan	nan
24	Hungary	HU	TOTAL	normalized	1.026484	0.967604	1.005912	1.000787	0.990451	1.097414	1.187944	1.041669	0.978737	0.968079	0.996162	vaccine	All	ratio (total_dose/pop.)	1.617727
25	Sweden	SE	TOTAL	normalized	1.019326	0.985579	0.995095	0.995218	0.956506	1.079097	0.985779	1.011546	1.005714	0.969874	0.995711	vaccine	All	ratio (total_dose/pop.)	4.336310
26	Croatia	HR	TOTAL	normalized	1.036057	0.963041	1.000902	0.987618	0.969749	1.088706	1.174273	1.066778	0.959584	0.946901	0.991087	vaccine	All	ratio (total_dose/pop.)	1.390181
27	Latvia	LV	TOTAL	normalized	1.009782	0.991345	0.998873	1.001801	0.963078	1.022469	1.201443	1.055301	0.962590	0.912575	0.976822	vaccine	All	ratio (total_dose/pop.)	1.503200
28	Bulgaria	BG	TOTAL	normalized	1.024011	0.977541	0.998447	0.988264	0.984702	1.150005	1.357649	1.080035	0.919335	0.911709	0.970360	vaccine	All	ratio (total_dose/pop.)	0.666504
29	Lithuania	LT	TOTAL	normalized	1.034162	0.993484	0.972354	0.959272	0.928441	1.077686	1.154059	1.030467	0.919153	0.914776	0.954799	vaccine	All	ratio (total_dose/pop.)	3.106103
30	Serbia	RS	TOTAL	normalized	1.024670	0.974227	1.001103	0.980439	0.979254	1.155481	1.318381	1.013430	0.917367	0.925909	0.952236	nan	nan	nan	nan
31	Romania	RO	TOTAL	normalized	1.020584	0.981718	0.997698	1.005356	0.993947	1.151277	1.276275	1.026653	0.918109	0.837779	0.927514	vaccine	All	ratio (total_dose/pop.)	0.817503
32	Armenia	AM	TOTAL	normalized	1.008985	1.007475	0.983540	0.920898	0.919641	1.278886	1.266846	0.964385	0.873495	0.916035	0.917971	nan	nan	nan	nan

Lets sort by the vaccines

	name	abbr	filter_x	value_type_x	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	average(2022-2024)	source	filter_y	value_type_y	value
4	Finland	FI	TOTAL	normalized	0.996378	1.003504	1.000118	1.016952	1.004682	1.051873	1.076956	1.155757	1.143393	1.078209	1.125786	vaccine	All	ratio (total_dose/pop.)	6.993552
8	Portugal	PT	TOTAL	normalized	1.006119	1.000327	0.993554	1.024547	1.013474	1.140328	1.128946	1.127418	1.071651	1.067896	1.088988	vaccine	All	ratio (total_dose/pop.)	5.294853
21	Italy	IT	TOTAL	normalized	1.028396	0.960289	1.011315	0.983592	0.989873	1.165225	1.087305	1.095037	1.013642	0.986208	1.031629	vaccine	All	ratio (total_dose/pop.)	4.765223
12	France	FR	TOTAL	normalized	1.007603	0.985137	1.007261	1.013398	1.019252	1.131754	1.099201	1.122302	1.061615	1.062469	1.082129	vaccine	All	ratio (total_dose/pop.)	4.584656
25	Sweden	SE	TOTAL	normalized	1.019326	0.985579	0.995095	0.995218	0.956506	1.079097	0.985779	1.011546	1.005714	0.969874	0.995711	vaccine	All	ratio (total_dose/pop.)	4.336310
29	Lithuania	LT	TOTAL	normalized	1.034162	0.993484	0.972354	0.959272	0.928441	1.077686	1.154059	1.030467	0.919153	0.914776	0.954799	vaccine	All	ratio (total_dose/pop.)	3.106103
18	Poland	PL	TOTAL	normalized	1.011934	0.974416	1.013649	1.035756	1.026469	1.222837	1.306178	1.125759	1.027167	1.015246	1.056057	vaccine	All	ratio (total_dose/pop.)	2.862610
22	Belgium	BE	TOTAL	normalized	1.024324	0.979883	0.995793	1.005480	0.988120	1.170333	1.020196	1.058305	1.012988	1.015740	1.029011	vaccine	All	ratio (total_dose/pop.)	2.411964
7	Denmark	DK	TOTAL	normalized	1.007454	0.991352	1.001194	1.038393	1.014429	1.045972	1.075440	1.117541	1.090769	1.064901	1.091070	vaccine	All	ratio (total_dose/pop.)	2.402740
0	Malta	MT	TOTAL	normalized	1.016949	0.955701	1.027350	1.060863	1.064330	1.199538	1.196071	1.219183	1.161980	1.183648	1.188270	vaccine	All	ratio (total_dose/pop.)	2.322112
10	Norway	NO	TOTAL	normalized	1.016140	0.989495	0.994364	0.996615	0.991477	1.008311	1.026049	1.115550	1.065612	1.072463	1.084542	vaccine	All	ratio (total_dose/pop.)	2.212603
5	Germany	DE	TOTAL	normalized	1.016481	0.979311	1.004209	1.029001	1.012227	1.082065	1.101127	1.149954	1.106922	1.079127	1.112001	vaccine	All	ratio (total_dose/pop.)	2.211555
16	Spain	ES	TOTAL	normalized	1.023104	0.971158	1.005738	1.013880	0.992168	1.192895	1.070740	1.101128	1.039692	1.042102	1.060974	vaccine	All	ratio (total_dose/pop.)	2.196480
3	Netherlands	NL	TOTAL	normalized	1.003373	0.993167	1.003460	1.024649	1.015107	1.147749	1.140424	1.133556	1.131250	1.143136	1.135981	vaccine	All	ratio (total_dose/pop.)	2.195485
6	Austria	AT	TOTAL	normalized	1.023795	0.972870	1.003336	1.012615	1.006371	1.125312	1.104853	1.130013	1.081778	1.067871	1.093221	vaccine	All	ratio (total_dose/pop.)	2.141405
2	Iceland	IS	TOTAL	normalized	0.988146	1.020151	0.991702	1.000148	1.005927	1.042377	1.039265	1.195733	1.140169	1.153060	1.162987	vaccine	All	ratio (total_dose/pop.)	2.138589
13	Greece	EL	TOTAL	normalized	1.012210	0.969465	1.018325	0.984534	1.022175	1.091374	1.179188	1.151479	1.047889	1.026689	1.075352	vaccine	All	ratio (total_dose/pop.)	2.032484
1	Cyprus	CY	TOTAL	normalized	1.029186	0.937156	1.033658	0.997362	1.069610	1.143922	1.241800	1.239908	1.150459	1.124312	1.171560	vaccine	All	ratio (total_dose/pop.)	1.988003
9	Luxembourg	LU	TOTAL	normalized	0.992076	0.966098	1.041827	1.053835	1.050895	1.143779	1.100400	1.089127	1.081529	1.095744	1.088800	vaccine	All	ratio (total_dose/pop.)	1.914499
15	Liechtenstein	LI	TOTAL	normalized	0.997423	1.043814	0.958763	1.055412	1.005155	1.256443	1.032216	1.078608	1.020619	1.117268	1.072165	vaccine	All	ratio (total_dose/pop.)	1.815763
20	Czechia	CZ	TOTAL	normalized	1.024453	0.970711	1.004837	1.019293	1.014188	1.190285	1.259588	1.084875	1.016894	1.003669	1.035146	vaccine	All	ratio (total_dose/pop.)	1.685242
24	Hungary	HU	TOTAL	normalized	1.026484	0.967604	1.005912	1.000787	0.990451	1.097414	1.187944	1.041669	0.978737	0.968079	0.996162	vaccine	All	ratio (total_dose/pop.)	1.617727
17	Estonia	EE	TOTAL	normalized	1.003717	0.992890	1.003393	1.016879	0.994057	1.041062	1.194009	1.115234	1.034320	1.030883	1.060146	vaccine	All	ratio (total_dose/pop.)	1.582880
27	Latvia	LV	TOTAL	normalized	1.009782	0.991345	0.998873	1.001801	0.963078	1.022469	1.201443	1.055301	0.962590	0.912575	0.976822	vaccine	All	ratio (total_dose/pop.)	1.503200
26	Croatia	HR	TOTAL	normalized	1.036057	0.963041	1.000902	0.987618	0.969749	1.088706	1.174273	1.066778	0.959584	0.946901	0.991087	vaccine	All	ratio (total_dose/pop.)	1.390181
11	Slovenia	SI	TOTAL	normalized	1.005897	0.975647	1.018456	1.018057	1.023988	1.216257	1.150672	1.118378	1.070087	1.061216	1.083227	vaccine	All	ratio (total_dose/pop.)	1.379628
19	Slovakia	SK	TOTAL	normalized	1.023092	0.973345	1.003563	1.011692	0.992593	1.126210	1.363524	1.109279	1.008254	0.995303	1.037612	vaccine	All	ratio (total_dose/pop.)	1.279721
31	Romania	RO	TOTAL	normalized	1.020584	0.981718	0.997698	1.005356	0.993947	1.151277	1.276275	1.026653	0.918109	0.837779	0.927514	vaccine	All	ratio (total_dose/pop.)	0.817503
28	Bulgaria	BG	TOTAL	normalized	1.024011	0.977541	0.998447	0.988264	0.984702	1.150005	1.357649	1.080035	0.919335	0.911709	0.970360	vaccine	All	ratio (total_dose/pop.)	0.666504
14	Switzerland	CH	TOTAL	normalized	1.031079	0.969066	0.999855	1.002628	1.012027	1.137130	1.058255	1.110659	1.069182	1.037839	1.072560	nan	nan	nan	nan
23	Montenegro	ME	TOTAL	normalized	0.999019	0.994684	1.006297	1.003200	1.018684	1.138065	1.411355	1.088052	0.977652	0.974413	1.013372	nan	nan	nan	nan
30	Serbia	RS	TOTAL	normalized	1.024670	0.974227	1.001103	0.980439	0.979254	1.155481	1.318381	1.013430	0.917367	0.925909	0.952236	nan	nan	nan	nan
32	Armenia	AM	TOTAL	normalized	1.008985	1.007475	0.983540	0.920898	0.919641	1.278886	1.266846	0.964385	0.873495	0.916035	0.917971	nan	nan	nan	nan

# deaths values with out the NA
d = dxv.copy().dropna(subset=['value'])
d = d.sort_values(by='average(2022-2024)',ascending=True)
d = d.reset_index()

# vaccines values with out the NA
v = dxv.copy().dropna(subset=['value'])
v = v.sort_values(by='value',ascending=True)
v = v.reset_index()
v['rank'] = v['value'].rank()

# making sure the lists have the same values
d = d[d.abbr.isin(v.abbr)]
d = d.reset_index(drop=True)

# display(d)
# display(v)

# Create a plot
fig, ax = plt.subplots(figsize=(14, 7))

ydistance = 0.95

# Plot points for the first dataframe
for i, row in d.iterrows():
    ax.scatter(0, i * ydistance, label=row['name'], color='blue')
    ax.text(-0.025, i * ydistance, row['name'] + ': ' + f"{row['average(2022-2024)']:.2f}", ha='right', va='center', color='blue')

# Plot points for the second dataframe
for i, row in v.iterrows():
    ax.scatter(1, i * ydistance, label=row['name'], color='red')
    ax.text(1.025, i * ydistance, row['name'] + ': ' + f"{row['value']:.2f}", ha='left', va='center', color='red')


ax.text(-0.025 - 1.0, len(v) * ydistance, 'most deaths')
ax.text(-0.025 - 1.0, 0 * ydistance, 'least deaths')

ax.text(1.025 + 1.0, len(v) * ydistance, 'most vaccines')
ax.text(1.025 + 1.0, 0 * ydistance, 'least vaccines')

# Create a colormap
colormap = plt.get_cmap('magma')
norm = Normalize(vmin=-np.pi, vmax=np.pi)

# Draw lines connecting matching abbr values
for name in d['name']:
    if name in v['name'].values:
        idx1 = d[d['name'] == name].index[0] * ydistance
        idx2 = v[v['name'] == name].index[0] * ydistance
        ax.plot([0, 1], [idx1, idx2], color='gray', linestyle='-')
        vertical_diff = idx2 - idx1
        horizontal_diff = 1  # The horizontal distance between the columns
        angle = np.arctan2(vertical_diff, horizontal_diff)  # Calculate the angle

        # normalized_angle = (angle + 2 * np.pi) / (2 * np.pi )  # Normalize angle to [0, 1]
        normalized_angle = pow ( abs(angle / np.pi)/0.5, 3) # custom normalized angle for better colors
        # print(normalized_angle)

        color = colormap(normalized_angle)  # Map normalized angle to a color
        ax.plot([0, 1], [idx1, idx2], color=color, linestyle='-')

# Formatting
ax.set_xlim(-0.5, 1.5)
ax.set_ylim(-1, max(len(d), len(v)))
ax.set_xticks([0, 1])
ax.set_xticklabels(['deaths average(2022-2024)', 'vaccines ratio (total_dose/pop.)'])
ax.set_yticks([])
ax.set_title('Mapping Between deaths and vaccines\n High values at the top, Low values at the bottom')
ax.grid(False)
plt.tight_layout()
plt.show()

Spearman and Kendall

# create a ranked list for deaths - average(2022-2024)
d = dxv.copy().reset_index(drop=True)
d = d.sort_values(by='average(2022-2024)',ascending=False) #most deaths to least deaths
d = d[['name','abbr','average(2022-2024)']]
d['rank'] = d['average(2022-2024)'].rank()

# create a ranked list for vaccines - ratio (total_dose/pop.)
v = dxv.copy().reset_index(drop=True)
v = v.sort_values(by='value',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','value']]
v['rank'] = v['value'].rank()

# make sure the have the same countries
d = d[d.abbr.isin(v.abbr)]

for i, row in v.iterrows():
    d.loc[d['abbr']==row['abbr'], 'rank'] = row['rank']

# no NAN values
d = d[~d['rank'].isna()]
v = v[~v['rank'].isna()]

display(MD(
'''### Output Interpretation

| Value     | Effect                      | Description/Correlation-Suggestion                         |
|-----------|-----------------------------|------------------------------------------------------------|
| **1.0**   | Positive effect             | Suggests more vaccines will lead to fewer deaths.          |
| **0.5**   | Slightly positive effect    | Slightly suggests more vaccines will lead to fewer deaths. |
| **0.0**   | Null/No effect              | Vaccines do not change the number of deaths.               |
| **-0.5**  | Slightly negative effect    | Slightly suggests more vaccines will lead to more deaths.  |
| **-1.0**  | Negative effect             | Suggests more vaccines will lead to more deaths.           |

'''
))




# Calculate Spearman's Rank Correlation
spearman_corr, _ = spearmanr(d['rank'], v['rank'])
display(MD(f"### Spearman's Rank Correlation: {spearman_corr}"))

display(bar(((spearman_corr+1.0)/2.0)*100,length=100))
display('-1'+' '*48 + '0' + ' '*50 + '1')

# Calculate Kendall's Tau
kendall_corr, _ = kendalltau(d['rank'], v['rank'])
display(MD(f"### Kendall's Tau: {kendall_corr}"))

display(bar(((kendall_corr+1.0)/2.0)*100,length=100))
display('-1'+' '*48 + '0' + ' '*50 + '1')

# display(d)
# display(v)

Output Interpretation

Value	Effect	Description/Correlation-Suggestion
1.0	Positive effect	Suggests more vaccines will lead to fewer deaths.
0.5	Slightly positive effect	Slightly suggests more vaccines will lead to fewer deaths.
0.0	Null/No effect	Vaccines do not change the number of deaths.
-0.5	Slightly negative effect	Slightly suggests more vaccines will lead to more deaths.
-1.0	Negative effect	Suggests more vaccines will lead to more deaths.

Spearman's Rank Correlation: -0.3177339901477832

'[##################################__________________________________________________________________]'

'-1                                                0                                                  1'

Kendall's Tau: -0.2364532019704433

'[######################################______________________________________________________________]'

'-1                                                0                                                  1'

Spearman and Kendall ... continued.

what agegroups can we filter by to get the max Spearman and Kendall Values?

display(MD(
'''### Output Interpretation

| Value     | Effect                      | Description/Correlation-Suggestion                         |
|-----------|-----------------------------|------------------------------------------------------------|
| **1.0**   | Positive effect             | Suggests more vaccines will lead to fewer deaths.          |
| **0.5**   | Slightly positive effect    | Slightly suggests more vaccines will lead to fewer deaths. |
| **0.0**   | Null/No effect              | Vaccines do not change the number of deaths.               |
| **-0.5**  | Slightly negative effect    | Slightly suggests more vaccines will lead to more deaths.  |
| **-1.0**  | Negative effect             | Suggests more vaccines will lead to more deaths.           |

'''
))

## vaccines in 2023
v2023 = cd[(cd['source']=='vaccine') & (cd['filter']=='All') & (cd['value_type']=='ratio (total_dose/pop.)') & (cd['year'] == 2023)]
v2023 = v2023[['name','source','filter','value_type','value']]

agegroups = ['Y_LT20','Y20-39', 'Y40-59', 'Y60-79', 'Y_GE80', ]

result = []

for ag in agegroups:
    temp = cd[(cd['source']=='deaths') & (cd['filter'].isin([ag])) & (cd['value_type']=='normalized')]
    temp = pd.pivot_table(
        data = temp,
        values = 'value',
        index = ['abbr','name','filter','value_type'],
        columns=['year'],
        aggfunc='mean',
    )
    temp.columns.name = 'index' 
    temp = temp.reset_index()
    temp['average(2022-2024)'] = (temp[2022]+temp[2023]+ temp[2024])/3.0
    temp = temp.sort_values(by='average(2022-2024)',ascending=False)

    temp = pd.merge(temp,v2023, how='left',on='name')

    
    # create a ranked list for deaths - average(2022-2024)
    d = temp.copy().reset_index(drop=True)
    d = d.sort_values(by='average(2022-2024)',ascending=False) #most deaths to least deaths
    d = d[['name','abbr','average(2022-2024)']]
    d['rank'] = d['average(2022-2024)'].rank()

    # create a ranked list for vaccines - ratio (total_dose/pop.)
    v = temp.copy().reset_index(drop=True)
    v = v.sort_values(by='value',ascending=True) #least vaccine to most vaccines
    v = v[['name','abbr','value']]
    v['rank'] = v['value'].rank()

    # make sure the have the same countries
    d = d[d.abbr.isin(v.abbr)]

    for i, row in v.iterrows():
        d.loc[d['abbr']==row['abbr'], 'rank'] = row['rank']

    # no NAN values
    d = d[~d['rank'].isna()]
    v = v[~v['rank'].isna()]

    # # Calculate Spearman's Rank Correlation
    spearman_corr, _ = spearmanr(d['rank'], v['rank'])


    # # Calculate Kendall's Tau
    kendall_corr, _ = kendalltau(d['rank'], v['rank'])

    result.append(
    {
        "agegroup":ag,
        "spearman_corr":spearman_corr,
        "kendall_corr":kendall_corr,
        }
    )

r = pd.DataFrame(result)
# r = r.sort_values(by='spearman_corr',ascending=False)
display(r.style.background_gradient(cmap=heatmapCM2, axis=0, subset=['spearman_corr']))

Output Interpretation

Value	Effect	Description/Correlation-Suggestion
1.0	Positive effect	Suggests more vaccines will lead to fewer deaths.
0.5	Slightly positive effect	Slightly suggests more vaccines will lead to fewer deaths.
0.0	Null/No effect	Vaccines do not change the number of deaths.
-0.5	Slightly negative effect	Slightly suggests more vaccines will lead to more deaths.
-1.0	Negative effect	Suggests more vaccines will lead to more deaths.

	agegroup	spearman_corr	kendall_corr
0	Y_LT20	-0.185714	-0.123153
1	Y20-39	-0.320197	-0.192118
2	Y40-59	0.029064	0.014778
3	Y60-79	-0.340394	-0.226601
4	Y_GE80	-0.230542	-0.172414

Other death x vaccine charts

temp = dxv[dxv.value < 14] #gets rid of the outliar (finland)
temp = temp.dropna(subset=['value'])


    # kde 
g = sns.jointplot(
    data=temp,
    x="value", 
    y="average(2022-2024)", 
    kind="kde",
    fill=True,
    cmap=heatmapCM
)

# Add scatter points on top
g.ax_joint.scatter(
    temp["value"], 
    temp['average(2022-2024)'], 
    color="black", 
    alpha=0.7, 
    s=7
)


g.set_axis_labels( "Vaccines (ratio (total_dose/pop.))","Deaths (average(2022-2024))")

g.figure.suptitle("Kde Plot : Death x Vaccines", fontsize=14)

# Add a title
# plt.title("<-- hypothetical 0 vaccines", fontsize=10,x=0.34,y=0.25)

# Add vertical and horizontal lines
plt.axvline(x=0.0, color='black', linestyle='--', linewidth=1)  # Vertical line at x=20
plt.axhline(y=1.0, color='black', linestyle='--', linewidth=1)  # Horizontal line at y=3

# display(vdravg)
plt.show()

lets look at Deaths for each year 2015-2024 separately

so the vaccine values will stay the same, but we are looking at the deaths overtime

temp = dxv[dxv.value < 14] #gets rid of the outliar (finland)
temp = temp.dropna(subset=['value'])

for y in [2015,2016,2017,2018,2019,2020,2021,2022,2023,2024]:
    # kde 
    g = sns.jointplot(
        data=temp,
        x="value", 
        y=y, 
        kind="kde",
        fill=True,
        cmap=heatmapCM
    )

    # Add scatter points on top
    g.ax_joint.scatter(
        temp["value"], 
        temp[y], 
        color="black", 
        alpha=0.7, 
        s=7
    )


    g.set_axis_labels( "Vaccines (ratio (total_dose/pop.))",f"Deaths ({y})")

    g.figure.suptitle(f"Kde Plot : Death({y}) x Vaccines", fontsize=14)

    # Add a title
    # plt.title("<-- hypothetical 0 vaccines", fontsize=10,x=0.34,y=0.25)

    # Add vertical and horizontal lines
    plt.axvline(x=0.0, color='black', linestyle='--', linewidth=1)  # Vertical line at x=20
    plt.axhline(y=1.0, color='black', linestyle='--', linewidth=1)  # Horizontal line at y=3

    # display(vdravg)
    plt.show()

notes:

• 2015-2019

  • provides the baseline

• 2020

  • the vaccine wasn't rolled out until the very end of this year, so the vaccines should have very little effect in here.

• 2021

  • this suggests that more vacccines caused less deaths.
  • i.e. dots move down when looking at them from left, to right. ↘️

• 2022

  • here it starts to change it's lean.

• 2023

  • here we can see a shift that more that the countries with more vaccines start to experience more deaths
  • i.e. dots move up when looking at them from left, to right. ↗️

• 2024

  • and this is similar to 2023
  • i.e. dots move up when looking at them from left, to right. ↗️

• and in 2022,2023,2024...we can see how they might average the to be the graph seen earlier.

title = 'Scatter Chart - Death x Vaccine'
display(MD(f'### {title}'))

fig = px.scatter(
        dxv,
        x="value",      
        y="average(2022-2024)",       
        color="name",      
        hover_name="name",
        title=title,
        labels={
            "value": "Vaccines (ratio total_dose/pop.)",  # Custom X-axis label
            "average(2022-2024)": "Deaths average(2022-2024)"           # Custom Y-axis label
        },
        height=750
    )
fig.update_layout(template="plotly_dark")

fig.add_shape(
    type="line",
    x0=0, x1=dxv.value.max(),
    y0=1, y1=1, # Start and end y-coordinates
    line=dict(color="Red", width=2, dash="dash"),  # Line style
    xref="x", yref="y"          # Reference axes
)


fig.show()

Scatter Chart - Death x Vaccine

Bad-Batch Theory

There seems to be to be an unusual grouping/cluster

Where countries that have a vaccine (ratio total_dose/pop.) between 4.0-6.0, have an unusually high Deaths average(2022-2024) above 1.1.

Bad-Batch Theory (by Vibeke Manniche)

A study in European Journal of Clinical Investigation found significant batch-dependent variations in adverse event reports for the Pfizer-BioNTech COVID-19 vaccine in Denmark (2020–2022),
suggesting some batches had higher reported risks. Further investigation is needed.

• The Paper

  • Batch-dependent safety of the BNT162b2 mRNA COVID-19 vaccine - by Vibeke Manniche

• An Interview

  • Excess deaths, the silence - John Campbell and Vibeke Manniche (Youtube)

• 

• 

• 

• 

my 2 cents

This is not suprising, the vaccine was created in a rushed manner ... and as a rule of thumb, Haste Makes Waste

• Ford Pinto (1971-1980)
• Windows Vista
• Samsung Galaxy Note 7
• Boeing 737 MAX
• ... and so many more

Lets look at some of the Causes of Death

note:
this data only does to 2023-ish.
with this limited amount of post-pandemic data
... it will be difficult to draw any meaningful conclusions.

title = 'Line Chart - normalized causes of death in Europe (by year)'
display(MD(f'### {title}'))

temp = cd[(cd['source']=='cause_of_death') & (~(cd['filter']=='TOTAL')) & (cd['value_type']=='normalized')]

temp = pd.pivot_table(
        data = temp,
        values = 'value',
        index = ['filter'],
        columns=['year'],
        aggfunc='mean',
    )
temp = temp.reset_index()

# temp['average(2021-2022)'] = (temp[2021]+temp[2022])/2.0
years = [i for i in range(2015,2023)]

# temp['stdev'] = temp[[2020,2021,2022]].std(axis=1)
# temp = temp.sort_values(by='stdev', ascending=False)

temp = temp.sort_values(by=2022, ascending=False)
display(temp.style.background_gradient(cmap=heatmapCM,axis=1,subset=years))

Line Chart - normalized causes of death in Europe (by year)

year	filter	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000
7	Diseases of the skin and subcutaneous tissue (L00-L99)	0.952700	0.988986	1.058314	1.075503	1.193232	1.258299	1.297455	1.591110	0.319839
12	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	0.995540	0.984867	1.019594	1.057114	1.219432	1.518470	1.339570	1.476779	0.290196
10	Mental and behavioural disorders (F00-F99)	0.931040	0.976116	1.092844	1.178312	1.225431	1.289635	1.347351	1.396606	0.221782
8	Endocrine, nutritional and metabolic diseases (E00-E90)	0.973747	0.979490	1.046763	1.070362	1.107944	1.249651	1.314197	1.272323	0.225376
4	Diseases of the genitourinary system (N00-N99)	0.992709	0.980498	1.026793	1.055125	1.045159	1.131820	1.178126	1.237250	0.273888
5	Diseases of the musculoskeletal system and connective tissue (M00-M99)	0.995449	0.958319	1.046231	1.105399	1.118685	1.068406	1.097093	1.139465	0.204976
3	Diseases of the digestive system (K00-K93)	0.988721	0.996419	1.014860	1.013569	1.038691	1.060206	1.109400	1.122434	0.222949
6	Diseases of the respiratory system (J00-J99)	0.991490	0.960160	1.048350	1.064795	1.023761	0.980327	0.985675	1.059276	0.222002
9	Malignant neoplasms (C00-C97)	0.994287	1.002868	1.002845	1.009227	1.008936	1.008924	0.992634	0.995533	0.196705
2	Diseases of the circulatory system (I00-I99)	1.021408	0.986229	0.992363	0.971170	0.947074	0.966720	0.980255	0.969827	0.181271
1	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	0.964858	1.030858	1.004284	1.007788	1.009398	0.988465	0.957862	0.950077	0.179165
11	Pregnancy, childbirth and the puerperium (O00-O99)	0.921207	1.079135	0.999658	0.881887	0.953946	0.962505	1.116414	0.928818	0.202308
0	Certain conditions originating in the perinatal period (P00-P96)	0.987427	1.015856	0.996718	0.953695	0.955849	0.880505	0.889766	0.851655	0.183221

Hypothesizing Adverse Reactions from an mRNA Vaccine

1. Blood Issues

   • Since the vaccine is injected into the body, potential blood-related adverse reactions should be considered.

2. Heart Issues

   • Based on reports and concerns about myocarditis and pericarditis.

3. DNA / Cancer Concerns (AKA Neoplasm)

   • Innate immune suppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs

     • A study suggests mRNA vaccines may suppress innate immunity by affecting type I interferon signaling, potentially increasing the risk of neurodegenerative diseases, myocarditis, immune disorders, and cancer.
       • Source: Innate immune suppression by SARS-CoV-2 mRNA vaccinations: The role of G-quadruplexes, exosomes, and MicroRNAs
       • From: National Library of Medicine
       • Authors: Stephanie Seneff, Greg Nigh, Anthony M. Kyriakopoulos, Peter A. McCullough

   • B-cell lymphoblastic lymphoma following intravenous BNT162b2 mRNA booster in a BALB/c mouse: A case report

     • A research group reported the sudden death of a mouse after its second Pfizer/BioNTech mRNA vaccine dose, which was found to have B-cell lymphoblastic lymphoma (fast-growing blood cancer).
       • Source: B-cell lymphoblastic lymphoma following intravenous BNT162b2 mRNA booster in a BALB/c mouse: A case report
       • From: frontiersin.org
       • Authors: Sander Eens, Manon Van Hecke , Kasper Favere , Thomas Tousseyn, Pieter-Jan Guns, Tania Roskams, Hein Heidbuchel

       • note: this has an addendum

         • "In conclusion, the novel COVID-19 vaccines have demonstrated an exceptional benefit-risk ratio in the fight against the pandemic and manifestations of severe adverse reactions following COVID-19 vaccination are rare."
           • however - the benefit-risk ratio value was not given.

   • DNA fragments detected in monovalent and bivalent 1Pfizer/BioNTech and Moderna modRNA COVID-19 vaccines 2from Ontario, Canada: Exploratory doseresponse 3relationship with serious adverse events.

     • These data demonstrate the presence of billions to hundreds of billions of DNA 537molecules per dosein the modRNA COVID-19 products tested.
       • Source: DNA fragments detected in monovalent and bivalent 1Pfizer/BioNTech and Moderna modRNA COVID-19 vaccines 2from Ontario, Canada: Exploratory doseresponse 3relationship with serious adverse events.
       • Authors: David J. Speicher, Jessica Rose,L. Maria Gutschi,David Wiseman, Kevin McKernan

   • COVID-19 Vaccines in People with Cancer

     • "There are some other types of vaccines that might not be safe for some people with cancer, but this depends on many factors, such as the type of vaccine, the type of cancer a person has (had), if they're still being treated for cancer, and if their immune system is working properly. Because of this, it’s best to talk with your doctor before getting any type of vaccine."
       • Source: COVID-19 Vaccines in People with Cancer
       • From: cancer.org

4. Unknown Issues

   • As a new medication, unforeseen adverse reactions may arise.

Spearman and Kendall ... (again)

# create a ranked list for vaccines - ratio (total_dose/pop.)

v = dxv.copy().reset_index(drop=True)
v = v.rename(columns={'value': 'vaccines | ratio (total_dose/pop.)'})
v = v[~v['vaccines | ratio (total_dose/pop.)'].isna()]
v = v.sort_values(by='vaccines | ratio (total_dose/pop.)',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','vaccines | ratio (total_dose/pop.)']]
v['rank'] = v['vaccines | ratio (total_dose/pop.)'].rank()
# display(v)

cods = cd[(cd.source == 'cause_of_death') & (cd.value_type == 'normalized') & (cd['year'] == 2022) & (~cd.value.isna()) & (~(cd['filter'] == 'Total')) ]['filter'].drop_duplicates().to_list()


result = []
for c in cods:
    # print(c)

    temp = pd.pivot_table(
        data = cd[(cd['filter'] == c) & (cd.value_type == 'normalized') & (cd['year'] == 2022)],
        values = 'value',
        index = ['name','abbr'],
        columns=['year'],
        aggfunc='mean',
    )
    temp = temp.reset_index()

    temp = temp.sort_values(by=2022,ascending=False) #most deaths to least deaths
    temp['rank'] = temp[2022].rank()

    # display(MD(f'#### {c}'))
    # display(temp)

    temp = temp[temp.abbr.isin(v.abbr)]
    vtemp = v[v.abbr.isin(temp.abbr)]

    for i, row in vtemp.iterrows():
        temp.loc[temp['abbr']==row['abbr'], 'rank'] = row['rank']

    result.append(
        {
            'cause_of_death': c,
            'Spearman_Rank': spearmanr(temp['rank'], vtemp['rank'])[0],
            'Kendall_Tau': kendalltau(temp['rank'], vtemp['rank'])[0]
        }
    )

    # display(temp)
    # display(vtemp)

    # display(MD(f"## {c} x Vaccines"))
    # # Calculate Spearman's Rank Correlation
    # spearman_corr, _ = spearmanr(temp['rank'], vtemp['rank'])
    # display(MD(f"### Spearman's Rank Correlation: {spearman_corr}"))

    # display(bar(((spearman_corr+1.0)/2.0)*100,length=100))
    # display('-1'+' '*48 + '0' + ' '*50 + '1')

    # # Calculate Kendall's Tau
    # kendall_corr, _ = kendalltau(temp['rank'], v['rank'])
    # display(MD(f"### Kendall's Tau: {kendall_corr}"))

    # display( bar(((kendall_corr+1.0)/2.0)*100,length=100))
    # display('-1'+' '*48 + '0' + ' '*50 + '1')

display(MD(
'''### Output Interpretation

| Value     | Effect                      | Description/Correlation-Suggestion                                   |
|-----------|-----------------------------|----------------------------------------------------------------------|
| **1.0**   | Positive effect             | Suggests more vaccines will lead to fewer [cause of death].          |
| **0.5**   | Slightly positive effect    | Slightly suggests more vaccines will lead to fewer [cause of death]. |
| **0.0**   | Null/No effect              | Vaccines do not change the number of [cause of death].               |
| **-0.5**  | Slightly negative effect    | Slightly suggests more vaccines will lead to more [cause of death].  |
| **-1.0**  | Negative effect             | Suggests more vaccines will lead to more [cause of death].           |

'''
))

result = pd.DataFrame(result)
result = result.sort_values(by='Spearman_Rank')
result = result.reset_index(drop=True)

display(MD("#### lets point out how many causes of death's increased with the number of vaccines"))
threshold = 0.29
display(MD(f"* {len(result[result['Spearman_Rank'] <= threshold*-1.0])} with a Spearman_Rank less than -{threshold}"))
display(MD(f"* {len(result[result['Spearman_Rank'] >= threshold])} with a Spearman_Rank greater than {threshold}"))

display(result.style.background_gradient(cmap=heatmapCM4)) #,vmax=1.0,vmin=-1.0))

# lets dataframe the top and bottom causes of death
TB_cod = pd.concat([result[result['Spearman_Rank'] <= threshold*-1.0],result[result['Spearman_Rank'] >= threshold]])

Output Interpretation

Value	Effect	Description/Correlation-Suggestion
1.0	Positive effect	Suggests more vaccines will lead to fewer [cause of death].
0.5	Slightly positive effect	Slightly suggests more vaccines will lead to fewer [cause of death].
0.0	Null/No effect	Vaccines do not change the number of [cause of death].
-0.5	Slightly negative effect	Slightly suggests more vaccines will lead to more [cause of death].
-1.0	Negative effect	Suggests more vaccines will lead to more [cause of death].

lets point out how many causes of death's increased with the number of vaccines

• 2 with a Spearman_Rank less than -0.29

• 1 with a Spearman_Rank greater than 0.29

	cause_of_death	Spearman_Rank	Kendall_Tau
0	Malignant neoplasms (C00-C97)	-0.483853	-0.317460
1	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	-0.447181	-0.333333
2	Pregnancy, childbirth and the puerperium (O00-O99)	-0.226957	-0.195652
3	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	-0.182813	-0.095238
4	Mental and behavioural disorders (F00-F99)	-0.132458	-0.068783
5	Diseases of the genitourinary system (N00-N99)	-0.076628	-0.026455
6	Diseases of the digestive system (K00-K93)	-0.045977	-0.031746
7	Diseases of the musculoskeletal system and connective tissue (M00-M99)	-0.045430	-0.058201
8	Endocrine, nutritional and metabolic diseases (E00-E90)	-0.015873	0.000000
9	Certain conditions originating in the perinatal period (P00-P96)	0.009852	0.015873
10	Diseases of the skin and subcutaneous tissue (L00-L99)	0.023536	0.037037
11	Diseases of the circulatory system (I00-I99)	0.145594	0.079365
12	Diseases of the respiratory system (J00-J99)	0.295567	0.211640

Note:

• Pregnancy, childbirth and the puerperium (O00-O99) only effects women (~50% of the population), so we should see that as double ... so account for that.
• the more vaccines the less Diseases of the respiratory system (J00-J99), however the spearman rank is low.
• Malignant neoplasms (C00-C97) has the highest absolute spearman ranking.

Lets see the Spearmans Rank over time

# create a ranked list for vaccines - ratio (total_dose/pop.)

v = dxv.copy().reset_index(drop=True)
v = v.rename(columns={'value': 'vaccines | ratio (total_dose/pop.)'})
v = v[~v['vaccines | ratio (total_dose/pop.)'].isna()]
v = v.sort_values(by='vaccines | ratio (total_dose/pop.)',ascending=True) #least vaccine to most vaccines
v = v[['name','abbr','vaccines | ratio (total_dose/pop.)']]
v['rank'] = v['vaccines | ratio (total_dose/pop.)'].rank()
# display(v)

cods = cd[(cd.source == 'cause_of_death') & (cd.value_type == 'normalized') & (cd['year'] == 2022) & (~cd.value.isna()) & (~(cd['filter'] == 'Total')) ]['filter'].drop_duplicates().to_list()

years = [i for i in range(2015,2023,1)]

result = []
for c in cods:
    
    for y in years:

        temp = pd.pivot_table(
            data = cd[(cd['filter'] == c) & (cd.value_type == 'normalized') & (cd['year'] == y)],
            values = 'value',
            index = ['name','abbr'],
            columns=['year'],
            aggfunc='mean',
        )
        temp = temp.reset_index()

        temp = temp.sort_values(by=y,ascending=False) #most deaths to least deaths
        temp['rank'] = temp[y].rank()
        # display(temp)

        temp = temp[temp.abbr.isin(v.abbr)]
        vtemp = v[v.abbr.isin(temp.abbr)]

        for i, row in vtemp.iterrows():
            temp.loc[temp['abbr']==row['abbr'], 'rank'] = row['rank']

        result.append(
            {
                'cause_of_death': c,
                'year': y,
                'Spearman_Rank': spearmanr(temp['rank'], vtemp['rank'])[0],
                'Kendall_Tau': kendalltau(temp['rank'], vtemp['rank'])[0]
            }
        )

display(MD(
'''### Output Interpretation

| Value     | Effect                      | Description/Correlation-Suggestion                                   |
|-----------|-----------------------------|----------------------------------------------------------------------|
| **1.0**   | Positive effect             | Suggests more vaccines will lead to fewer [cause of death].          |
| **0.5**   | Slightly positive effect    | Slightly suggests more vaccines will lead to fewer [cause of death]. |
| **0.0**   | Null/No effect              | Vaccines do not change the number of [cause of death].               |
| **-0.5**  | Slightly negative effect    | Slightly suggests more vaccines will lead to more [cause of death].  |
| **-1.0**  | Negative effect             | Suggests more vaccines will lead to more [cause of death].           |

'''
))

result = pd.DataFrame(result)
result = result.sort_values(by='Spearman_Rank')
result = result.reset_index(drop=True)

display(MD("#### lets point out how many causes of death's increased with the number of vaccines"))
display(MD(f"* {len(result[result['Spearman_Rank'] <= -0.3])} with a Spearman_Rank less than -0.3"))
display(MD(f"* {len(result[result['Spearman_Rank'] >= 0.3])} with a Spearman_Rank greater than 0.3"))

for y in years:
    display(MD(f'##### year = {y}'))
    display(result[result['year']==y].style.background_gradient(cmap=heatmapCM4,subset=['Spearman_Rank','Kendall_Tau'])) #,vmax=1.0,vmin=-1.0))

Output Interpretation

Value	Effect	Description/Correlation-Suggestion
1.0	Positive effect	Suggests more vaccines will lead to fewer [cause of death].
0.5	Slightly positive effect	Slightly suggests more vaccines will lead to fewer [cause of death].
0.0	Null/No effect	Vaccines do not change the number of [cause of death].
-0.5	Slightly negative effect	Slightly suggests more vaccines will lead to more [cause of death].
-1.0	Negative effect	Suggests more vaccines will lead to more [cause of death].

lets point out how many causes of death's increased with the number of vaccines

• 9 with a Spearman_Rank less than -0.3

• 8 with a Spearman_Rank greater than 0.3

year = 2015

	cause_of_death	year	Spearman_Rank	Kendall_Tau
8	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2015	-0.317460	-0.243386
11	Diseases of the genitourinary system (N00-N99)	2015	-0.292830	-0.190476
34	Certain conditions originating in the perinatal period (P00-P96)	2015	-0.068418	-0.063492
44	Diseases of the circulatory system (I00-I99)	2015	-0.005473	-0.015873
52	Diseases of the digestive system (K00-K93)	2015	0.022989	0.005291
56	Diseases of the respiratory system (J00-J99)	2015	0.035577	0.021164
58	Endocrine, nutritional and metabolic diseases (E00-E90)	2015	0.047619	0.042328
59	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2015	0.056377	0.005291
61	Mental and behavioural disorders (F00-F99)	2015	0.084291	0.084656
65	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2015	0.092501	0.052910
81	Diseases of the skin and subcutaneous tissue (L00-L99)	2015	0.152162	0.095238
91	Malignant neoplasms (C00-C97)	2015	0.235906	0.169312
92	Pregnancy, childbirth and the puerperium (O00-O99)	2015	0.241739	0.144928

year = 2016

	cause_of_death	year	Spearman_Rank	Kendall_Tau
9	Diseases of the circulatory system (I00-I99)	2016	-0.293924	-0.211640
10	Malignant neoplasms (C00-C97)	2016	-0.293377	-0.185185
12	Diseases of the digestive system (K00-K93)	2016	-0.282430	-0.185185
22	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2016	-0.165846	-0.111111
25	Diseases of the respiratory system (J00-J99)	2016	-0.138478	-0.132275
29	Mental and behavioural disorders (F00-F99)	2016	-0.121511	-0.132275
30	Certain conditions originating in the perinatal period (P00-P96)	2016	-0.116585	-0.084656
45	Diseases of the genitourinary system (N00-N99)	2016	-0.003831	0.015873
46	Diseases of the skin and subcutaneous tissue (L00-L99)	2016	-0.001642	-0.042328
57	Pregnancy, childbirth and the puerperium (O00-O99)	2016	0.039130	0.057971
60	Endocrine, nutritional and metabolic diseases (E00-E90)	2016	0.082102	0.052910
82	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2016	0.157088	0.132275
86	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2016	0.206349	0.164021

year = 2017

	cause_of_death	year	Spearman_Rank	Kendall_Tau
6	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2017	-0.333881	-0.227513
13	Pregnancy, childbirth and the puerperium (O00-O99)	2017	-0.254783	-0.210145
23	Mental and behavioural disorders (F00-F99)	2017	-0.153257	-0.089947
35	Malignant neoplasms (C00-C97)	2017	-0.062945	-0.042328
37	Endocrine, nutritional and metabolic diseases (E00-E90)	2017	-0.055829	-0.058201
38	Diseases of the skin and subcutaneous tissue (L00-L99)	2017	-0.053640	-0.047619
63	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2017	0.085933	0.084656
74	Diseases of the digestive system (K00-K93)	2017	0.122605	0.063492
75	Certain conditions originating in the perinatal period (P00-P96)	2017	0.125889	0.105820
77	Diseases of the respiratory system (J00-J99)	2017	0.144499	0.089947
85	Diseases of the genitourinary system (N00-N99)	2017	0.198139	0.137566
97	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2017	0.347564	0.259259
102	Diseases of the circulatory system (I00-I99)	2017	0.407772	0.227513

year = 2018

	cause_of_death	year	Spearman_Rank	Kendall_Tau
2	Pregnancy, childbirth and the puerperium (O00-O99)	2018	-0.426087	-0.304348
16	Certain conditions originating in the perinatal period (P00-P96)	2018	-0.210181	-0.158730
17	Mental and behavioural disorders (F00-F99)	2018	-0.200876	-0.148148
19	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2018	-0.191571	-0.142857
21	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2018	-0.170772	-0.116402
27	Diseases of the skin and subcutaneous tissue (L00-L99)	2018	-0.131910	-0.089947
51	Malignant neoplasms (C00-C97)	2018	0.018610	-0.005291
66	Diseases of the circulatory system (I00-I99)	2018	0.096333	0.026455
71	Diseases of the respiratory system (J00-J99)	2018	0.105090	0.084656
76	Diseases of the genitourinary system (N00-N99)	2018	0.134100	0.074074
84	Endocrine, nutritional and metabolic diseases (E00-E90)	2018	0.191024	0.095238
99	Diseases of the digestive system (K00-K93)	2018	0.377121	0.253968
100	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2018	0.381500	0.248677

year = 2019

	cause_of_death	year	Spearman_Rank	Kendall_Tau
4	Pregnancy, childbirth and the puerperium (O00-O99)	2019	-0.365217	-0.260870
14	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2019	-0.254516	-0.148148
18	Malignant neoplasms (C00-C97)	2019	-0.199781	-0.132275
28	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2019	-0.126437	-0.084656
50	Diseases of the skin and subcutaneous tissue (L00-L99)	2019	0.014778	0.021164
54	Diseases of the respiratory system (J00-J99)	2019	0.024083	-0.010582
55	Mental and behavioural disorders (F00-F99)	2019	0.032841	0.021164
67	Diseases of the digestive system (K00-K93)	2019	0.097975	0.074074
73	Certain conditions originating in the perinatal period (P00-P96)	2019	0.117132	0.095238
83	Endocrine, nutritional and metabolic diseases (E00-E90)	2019	0.176793	0.111111
88	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2019	0.221675	0.148148
90	Diseases of the genitourinary system (N00-N99)	2019	0.230980	0.164021
94	Diseases of the circulatory system (I00-I99)	2019	0.282978	0.201058

year = 2020

	cause_of_death	year	Spearman_Rank	Kendall_Tau
7	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2020	-0.329502	-0.222222
31	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2020	-0.087575	-0.052910
33	Diseases of the digestive system (K00-K93)	2020	-0.072250	-0.037037
41	Diseases of the skin and subcutaneous tissue (L00-L99)	2020	-0.029009	-0.010582
42	Malignant neoplasms (C00-C97)	2020	-0.025725	0.026455
47	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2020	0.007115	-0.037037
62	Certain conditions originating in the perinatal period (P00-P96)	2020	0.085386	0.047619
69	Pregnancy, childbirth and the puerperium (O00-O99)	2020	0.101739	0.057971
70	Mental and behavioural disorders (F00-F99)	2020	0.104543	0.063492
87	Endocrine, nutritional and metabolic diseases (E00-E90)	2020	0.220033	0.164021
89	Diseases of the respiratory system (J00-J99)	2020	0.224412	0.164021
93	Diseases of the genitourinary system (N00-N99)	2020	0.245758	0.137566
96	Diseases of the circulatory system (I00-I99)	2020	0.315818	0.227513

year = 2021

	cause_of_death	year	Spearman_Rank	Kendall_Tau
3	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2021	-0.392447	-0.306878
5	Malignant neoplasms (C00-C97)	2021	-0.357964	-0.280423
24	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2021	-0.143404	-0.084656
36	Certain conditions originating in the perinatal period (P00-P96)	2021	-0.056924	-0.021164
48	Diseases of the digestive system (K00-K93)	2021	0.007115	0.015873
64	Mental and behavioural disorders (F00-F99)	2021	0.091954	0.042328
68	Diseases of the skin and subcutaneous tissue (L00-L99)	2021	0.098522	0.063492
72	Pregnancy, childbirth and the puerperium (O00-O99)	2021	0.110435	0.065217
79	Diseases of the genitourinary system (N00-N99)	2021	0.148878	0.100529
80	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2021	0.151067	0.089947
98	Endocrine, nutritional and metabolic diseases (E00-E90)	2021	0.363437	0.291005
101	Diseases of the circulatory system (I00-I99)	2021	0.403394	0.285714
103	Diseases of the respiratory system (J00-J99)	2021	0.451560	0.338624

year = 2022

	cause_of_death	year	Spearman_Rank	Kendall_Tau
0	Malignant neoplasms (C00-C97)	2022	-0.483853	-0.317460
1	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	2022	-0.447181	-0.333333
15	Pregnancy, childbirth and the puerperium (O00-O99)	2022	-0.226957	-0.195652
20	Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified (R00-R99)	2022	-0.182813	-0.095238
26	Mental and behavioural disorders (F00-F99)	2022	-0.132458	-0.068783
32	Diseases of the genitourinary system (N00-N99)	2022	-0.076628	-0.026455
39	Diseases of the digestive system (K00-K93)	2022	-0.045977	-0.031746
40	Diseases of the musculoskeletal system and connective tissue (M00-M99)	2022	-0.045430	-0.058201
43	Endocrine, nutritional and metabolic diseases (E00-E90)	2022	-0.015873	0.000000
49	Certain conditions originating in the perinatal period (P00-P96)	2022	0.009852	0.015873
53	Diseases of the skin and subcutaneous tissue (L00-L99)	2022	0.023536	0.037037
78	Diseases of the circulatory system (I00-I99)	2022	0.145594	0.079365
95	Diseases of the respiratory system (J00-J99)	2022	0.295567	0.211640

title = 'Line Chart - Cause of Death x Spearmans Rank'
display(MD(f'### {title}'))

temp = result.copy()
temp = temp.sort_values(by='Spearman_Rank', ascending=True)
temp = temp.sort_values(by='year', ascending=True)

fig = px.line(
    temp,
    x='year',
    y='Spearman_Rank',
    color='cause_of_death',
    height=750 ,
    title=title
    )
fig.update_layout(template="plotly_dark")

Line Chart - Cause of Death x Spearmans Rank

Note:

in the Line Chart - Cause of Death x Spearmans Rank chart above, from 2021-2022 there are many Causes of Death going in the downward (in the negative direction).
I'm wondering if this will continue.

lets see these overtime

# lets also filter by the top 10 most and least 10 vaccinated countries
v = dxv.copy().reset_index(drop=True)
v = v.sort_values(by='value',ascending=False) #least vaccine to most vaccines
v = v[~v['value'].isna()]
# include_abbr = pd.concat([v.head(5),v.tail(5)]).abbr.to_list()
include_abbr = v.abbr.drop_duplicates().to_list()

# let's print this lists for reference
# display(v.head(5))
# display(v.tail(5))

for index,row in TB_cod.iterrows():
    print(index,row)

    temp = cd[(cd['source']=='cause_of_death') & (cd['filter'] == row['cause_of_death']) & (cd['value_type']=='normalized') & (cd['year'] < 2023)]
    temp = temp[temp['abbr'].isin(include_abbr)]

    ## adding the vaccine value (ratio (total_dose/pop.))
    temp['vac dose/pop.'] = 0.0
    for i, vrow in v.iterrows():
        mask = temp['abbr'] == vrow['abbr']  # Create a boolean mask
        temp.loc[mask, 'vac dose/pop.'] = vrow['value']

    temp = temp.sort_values(by=['year', 'name'], ascending=[True, True])

    fig = px.line(
        temp,
        x='year',
        y='value',
        color='name',
        height=750 ,
        hover_data={
            'name', 'abbr', 'value','vac dose/pop.'
        },
        title=f"cause_of_death \'{row['cause_of_death']}\' , Spearman_Rank {row['Spearman_Rank']}"
        )
    fig.update_layout(template="plotly_dark")
    fig.add_shape(
        type="line",
        x0=2015, x1=2022,
        y0=1, y1=1, # Start and end y-coordinates
        line=dict(color="Red", width=2, dash="dash"),  # Line style
        xref="x", yref="y"          # Reference axes
    )
    fig.show()

0 cause_of_death    Malignant neoplasms (C00-C97)
Spearman_Rank                            -0.484
Kendall_Tau                              -0.317
Name: 0, dtype: object

1 cause_of_death    Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)
Spearman_Rank                                                                             -0.447
Kendall_Tau                                                                               -0.333
Name: 1, dtype: object

12 cause_of_death    Diseases of the respiratory system (J00-J99)
Spearman_Rank                                            0.296
Kendall_Tau                                              0.212
Name: 12, dtype: object

temp = cd[(cd['source']=='cause_of_death') & (cd['value_type']=='normalized')]

codlist = ['Malignant neoplasms (C00-C97)','Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)','Diseases of the respiratory system (J00-J99)']
temp = temp[temp['filter'].isin(codlist)]

temp = pd.pivot_table(
    data = temp,
    values = 'value',
    index = ['abbr','name','filter','value_type'],
    columns=['year'],
    aggfunc='mean',
)
temp.columns.name = 'index' 
temp = temp.reset_index()

## vaccines in 2023
v2023 = cd[(cd['source']=='vaccine') & (cd['filter']=='All') & (cd['value_type']=='ratio (total_dose/pop.)') & (cd['year'] == 2023)]
v2023 = v2023[['name','source','filter','value_type','value']]

# adding it to the temp
temp = pd.merge(temp,v2023, how='left',on='name')


for c in codlist:
    display(MD(f'### {c}'))
    temp2 = temp[temp['filter_x']==c]
    temp2 = temp2.sort_values(by=2022,ascending=False)

    styled_df = temp2.style.background_gradient(cmap=heatmapCM2, axis=1, subset=[i for i in range(2015,2023)]) \
                        .background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])
    
    display(MD('#### sorted by 2022 column'))
    display(styled_df)
    
    temp2 = temp2.sort_values(by='value',ascending=False)

    styled_df = temp2.style.background_gradient(cmap=heatmapCM2, axis=1, subset=[i for i in range(2015,2023)]) \
                        .background_gradient(cmap=heatmapCM3, axis=0, subset=['value'])
    
    display(MD('#### sorted by value (ratio (total_dose/pop.))'))
    display(styled_df)

# we will use this in a cell below
codxv = temp.copy()

Malignant neoplasms (C00-C97)

sorted by 2022 column

	abbr	name	filter_x	value_type_x	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000	source	filter_y	value_type_y	value
14	CY	Cyprus	Malignant neoplasms (C00-C97)	normalized	0.991532	0.951183	1.057285	1.068493	1.072229	1.133499	1.188045	1.117808	0.000000	vaccine	All	ratio (total_dose/pop.)	1.988003
47	IE	Ireland	Malignant neoplasms (C00-C97)	normalized	0.979477	1.011917	1.008606	1.021516	1.056383	1.064548	1.063886	1.114421	0.000000	vaccine	All	ratio (total_dose/pop.)	2.405531
65	MT	Malta	Malignant neoplasms (C00-C97)	normalized	0.988223	0.994647	1.017131	1.119914	1.016060	1.077088	1.046039	1.099572	0.000000	vaccine	All	ratio (total_dose/pop.)	2.322112
50	IS	Iceland	Malignant neoplasms (C00-C97)	normalized	0.964978	1.084591	0.950431	0.934267	1.021552	1.005388	0.995690	1.087823	1.076509	vaccine	All	ratio (total_dose/pop.)	2.138589
35	FI	Finland	Malignant neoplasms (C00-C97)	normalized	0.980161	1.006497	1.013343	1.012135	1.039357	1.047572	1.060377	1.053854	0.000000	vaccine	All	ratio (total_dose/pop.)	6.993552
2	AT	Austria	Malignant neoplasms (C00-C97)	normalized	1.004749	0.995967	0.999284	1.013433	1.012262	1.031582	1.018751	1.041828	1.038022	vaccine	All	ratio (total_dose/pop.)	2.141405
71	NO	Norway	Malignant neoplasms (C00-C97)	normalized	0.991960	1.001995	1.006045	1.005861	0.999417	1.002639	1.007702	1.041121	0.000000	vaccine	All	ratio (total_dose/pop.)	2.212603
77	PT	Portugal	Malignant neoplasms (C00-C97)	normalized	0.980787	1.006920	1.012293	1.027973	1.050609	1.045051	1.017483	1.028120	0.000000	vaccine	All	ratio (total_dose/pop.)	5.294853
32	ES	Spain	Malignant neoplasms (C00-C97)	normalized	0.989611	1.003440	1.006949	1.001899	1.005047	1.001963	1.009764	1.021535	1.027951	vaccine	All	ratio (total_dose/pop.)	2.196480
68	NL	Netherlands	Malignant neoplasms (C00-C97)	normalized	0.986301	1.011021	1.002677	0.999473	1.007528	1.006549	1.006104	1.018831	0.000000	vaccine	All	ratio (total_dose/pop.)	2.195485
17	CZ	Czechia	Malignant neoplasms (C00-C97)	normalized	0.989232	1.004760	1.006008	1.019701	1.038570	1.032917	0.996867	1.016911	1.006669	vaccine	All	ratio (total_dose/pop.)	1.685242
89	SI	Slovenia	Malignant neoplasms (C00-C97)	normalized	0.990301	0.996979	1.012721	1.042455	0.997138	1.021784	0.986961	1.015742	0.000000	vaccine	All	ratio (total_dose/pop.)	1.379628
20	DE	Germany	Malignant neoplasms (C00-C97)	normalized	0.992060	1.010891	0.997049	1.007774	1.013348	1.012863	1.002733	1.014257	0.000000	vaccine	All	ratio (total_dose/pop.)	2.211555
23	DK	Denmark	Malignant neoplasms (C00-C97)	normalized	0.987781	1.011301	1.000919	1.003931	1.032706	1.011749	1.031360	1.011621	0.000000	vaccine	All	ratio (total_dose/pop.)	2.402740
38	FR	France	Malignant neoplasms (C00-C97)	normalized	0.991905	1.004069	1.004026	0.997865	1.002613	1.001065	0.995754	1.007122	0.000000	vaccine	All	ratio (total_dose/pop.)	4.584656
11	CH	Switzerland	Malignant neoplasms (C00-C97)	normalized	1.001197	0.997778	1.001026	1.005698	0.997493	0.977379	0.973789	0.995840	0.000000	nan	nan	nan	nan
86	SE	Sweden	Malignant neoplasms (C00-C97)	normalized	0.989690	0.996485	1.013825	0.995911	0.993396	0.997676	0.980910	0.993352	0.000000	vaccine	All	ratio (total_dose/pop.)	4.336310
5	BE	Belgium	Malignant neoplasms (C00-C97)	normalized	1.008534	1.008903	0.982563	0.977000	0.983594	0.967091	0.965801	0.971511	0.000000	vaccine	All	ratio (total_dose/pop.)	2.411964
56	LT	Lithuania	Malignant neoplasms (C00-C97)	normalized	1.019847	1.002132	0.978020	0.980727	0.982572	1.003855	0.948864	0.970147	0.000000	vaccine	All	ratio (total_dose/pop.)	3.106103
53	IT	Italy	Malignant neoplasms (C00-C97)	normalized	0.999683	0.999372	1.000945	1.001978	0.994882	0.987206	0.969412	0.967980	0.000000	vaccine	All	ratio (total_dose/pop.)	4.765223
29	EL	Greece	Malignant neoplasms (C00-C97)	normalized	0.993818	1.006138	1.000045	0.995358	1.004229	1.010891	1.007577	0.966031	0.000000	vaccine	All	ratio (total_dose/pop.)	2.032484
74	PL	Poland	Malignant neoplasms (C00-C97)	normalized	1.005447	0.998825	0.995728	1.012948	1.002550	0.997746	0.935670	0.960480	0.000000	vaccine	All	ratio (total_dose/pop.)	2.862610
59	LU	Luxembourg	Malignant neoplasms (C00-C97)	normalized	0.979083	0.997539	1.023377	0.993848	1.018763	0.939403	0.979083	0.958782	0.977238	vaccine	All	ratio (total_dose/pop.)	1.914499
62	LV	Latvia	Malignant neoplasms (C00-C97)	normalized	0.991956	0.996512	1.011532	1.002250	1.000225	1.012544	0.971367	0.958204	0.000000	vaccine	All	ratio (total_dose/pop.)	1.503200
92	SK	Slovakia	Malignant neoplasms (C00-C97)	normalized	0.997125	0.998441	1.004434	1.026214	0.991863	1.033742	0.951958	0.941214	0.971472	vaccine	All	ratio (total_dose/pop.)	1.279721
41	HR	Croatia	Malignant neoplasms (C00-C97)	normalized	1.005125	1.010202	0.984673	1.001335	0.966725	0.952065	0.957858	0.935618	0.000000	vaccine	All	ratio (total_dose/pop.)	1.390181
44	HU	Hungary	Malignant neoplasms (C00-C97)	normalized	0.997496	1.003427	0.999077	0.991229	0.973769	0.961936	0.930604	0.926437	0.000000	vaccine	All	ratio (total_dose/pop.)	1.617727
8	BG	Bulgaria	Malignant neoplasms (C00-C97)	normalized	1.025194	0.983432	0.991373	0.992287	1.036849	1.051645	0.977034	0.925674	0.000000	vaccine	All	ratio (total_dose/pop.)	0.666504
26	EE	Estonia	Malignant neoplasms (C00-C97)	normalized	1.010756	0.987146	1.002099	1.025184	0.998951	0.957765	0.969307	0.913431	0.000000	vaccine	All	ratio (total_dose/pop.)	1.582880
83	RS	Serbia	Malignant neoplasms (C00-C97)	normalized	0.996101	1.003138	1.000761	1.006913	0.994470	0.967767	0.931046	0.901734	0.000000	nan	nan	nan	nan
80	RO	Romania	Malignant neoplasms (C00-C97)	normalized	0.992785	1.003276	1.003938	1.000454	0.971862	0.961682	0.893871	0.884509	0.000000	vaccine	All	ratio (total_dose/pop.)	0.817503

sorted by value (ratio (total_dose/pop.))

	abbr	name	filter_x	value_type_x	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000	source	filter_y	value_type_y	value
35	FI	Finland	Malignant neoplasms (C00-C97)	normalized	0.980161	1.006497	1.013343	1.012135	1.039357	1.047572	1.060377	1.053854	0.000000	vaccine	All	ratio (total_dose/pop.)	6.993552
77	PT	Portugal	Malignant neoplasms (C00-C97)	normalized	0.980787	1.006920	1.012293	1.027973	1.050609	1.045051	1.017483	1.028120	0.000000	vaccine	All	ratio (total_dose/pop.)	5.294853
53	IT	Italy	Malignant neoplasms (C00-C97)	normalized	0.999683	0.999372	1.000945	1.001978	0.994882	0.987206	0.969412	0.967980	0.000000	vaccine	All	ratio (total_dose/pop.)	4.765223
38	FR	France	Malignant neoplasms (C00-C97)	normalized	0.991905	1.004069	1.004026	0.997865	1.002613	1.001065	0.995754	1.007122	0.000000	vaccine	All	ratio (total_dose/pop.)	4.584656
86	SE	Sweden	Malignant neoplasms (C00-C97)	normalized	0.989690	0.996485	1.013825	0.995911	0.993396	0.997676	0.980910	0.993352	0.000000	vaccine	All	ratio (total_dose/pop.)	4.336310
56	LT	Lithuania	Malignant neoplasms (C00-C97)	normalized	1.019847	1.002132	0.978020	0.980727	0.982572	1.003855	0.948864	0.970147	0.000000	vaccine	All	ratio (total_dose/pop.)	3.106103
74	PL	Poland	Malignant neoplasms (C00-C97)	normalized	1.005447	0.998825	0.995728	1.012948	1.002550	0.997746	0.935670	0.960480	0.000000	vaccine	All	ratio (total_dose/pop.)	2.862610
5	BE	Belgium	Malignant neoplasms (C00-C97)	normalized	1.008534	1.008903	0.982563	0.977000	0.983594	0.967091	0.965801	0.971511	0.000000	vaccine	All	ratio (total_dose/pop.)	2.411964
47	IE	Ireland	Malignant neoplasms (C00-C97)	normalized	0.979477	1.011917	1.008606	1.021516	1.056383	1.064548	1.063886	1.114421	0.000000	vaccine	All	ratio (total_dose/pop.)	2.405531
23	DK	Denmark	Malignant neoplasms (C00-C97)	normalized	0.987781	1.011301	1.000919	1.003931	1.032706	1.011749	1.031360	1.011621	0.000000	vaccine	All	ratio (total_dose/pop.)	2.402740
65	MT	Malta	Malignant neoplasms (C00-C97)	normalized	0.988223	0.994647	1.017131	1.119914	1.016060	1.077088	1.046039	1.099572	0.000000	vaccine	All	ratio (total_dose/pop.)	2.322112
71	NO	Norway	Malignant neoplasms (C00-C97)	normalized	0.991960	1.001995	1.006045	1.005861	0.999417	1.002639	1.007702	1.041121	0.000000	vaccine	All	ratio (total_dose/pop.)	2.212603
20	DE	Germany	Malignant neoplasms (C00-C97)	normalized	0.992060	1.010891	0.997049	1.007774	1.013348	1.012863	1.002733	1.014257	0.000000	vaccine	All	ratio (total_dose/pop.)	2.211555
32	ES	Spain	Malignant neoplasms (C00-C97)	normalized	0.989611	1.003440	1.006949	1.001899	1.005047	1.001963	1.009764	1.021535	1.027951	vaccine	All	ratio (total_dose/pop.)	2.196480
68	NL	Netherlands	Malignant neoplasms (C00-C97)	normalized	0.986301	1.011021	1.002677	0.999473	1.007528	1.006549	1.006104	1.018831	0.000000	vaccine	All	ratio (total_dose/pop.)	2.195485
2	AT	Austria	Malignant neoplasms (C00-C97)	normalized	1.004749	0.995967	0.999284	1.013433	1.012262	1.031582	1.018751	1.041828	1.038022	vaccine	All	ratio (total_dose/pop.)	2.141405
50	IS	Iceland	Malignant neoplasms (C00-C97)	normalized	0.964978	1.084591	0.950431	0.934267	1.021552	1.005388	0.995690	1.087823	1.076509	vaccine	All	ratio (total_dose/pop.)	2.138589
29	EL	Greece	Malignant neoplasms (C00-C97)	normalized	0.993818	1.006138	1.000045	0.995358	1.004229	1.010891	1.007577	0.966031	0.000000	vaccine	All	ratio (total_dose/pop.)	2.032484
14	CY	Cyprus	Malignant neoplasms (C00-C97)	normalized	0.991532	0.951183	1.057285	1.068493	1.072229	1.133499	1.188045	1.117808	0.000000	vaccine	All	ratio (total_dose/pop.)	1.988003
59	LU	Luxembourg	Malignant neoplasms (C00-C97)	normalized	0.979083	0.997539	1.023377	0.993848	1.018763	0.939403	0.979083	0.958782	0.977238	vaccine	All	ratio (total_dose/pop.)	1.914499
17	CZ	Czechia	Malignant neoplasms (C00-C97)	normalized	0.989232	1.004760	1.006008	1.019701	1.038570	1.032917	0.996867	1.016911	1.006669	vaccine	All	ratio (total_dose/pop.)	1.685242
44	HU	Hungary	Malignant neoplasms (C00-C97)	normalized	0.997496	1.003427	0.999077	0.991229	0.973769	0.961936	0.930604	0.926437	0.000000	vaccine	All	ratio (total_dose/pop.)	1.617727
26	EE	Estonia	Malignant neoplasms (C00-C97)	normalized	1.010756	0.987146	1.002099	1.025184	0.998951	0.957765	0.969307	0.913431	0.000000	vaccine	All	ratio (total_dose/pop.)	1.582880
62	LV	Latvia	Malignant neoplasms (C00-C97)	normalized	0.991956	0.996512	1.011532	1.002250	1.000225	1.012544	0.971367	0.958204	0.000000	vaccine	All	ratio (total_dose/pop.)	1.503200
41	HR	Croatia	Malignant neoplasms (C00-C97)	normalized	1.005125	1.010202	0.984673	1.001335	0.966725	0.952065	0.957858	0.935618	0.000000	vaccine	All	ratio (total_dose/pop.)	1.390181
89	SI	Slovenia	Malignant neoplasms (C00-C97)	normalized	0.990301	0.996979	1.012721	1.042455	0.997138	1.021784	0.986961	1.015742	0.000000	vaccine	All	ratio (total_dose/pop.)	1.379628
92	SK	Slovakia	Malignant neoplasms (C00-C97)	normalized	0.997125	0.998441	1.004434	1.026214	0.991863	1.033742	0.951958	0.941214	0.971472	vaccine	All	ratio (total_dose/pop.)	1.279721
80	RO	Romania	Malignant neoplasms (C00-C97)	normalized	0.992785	1.003276	1.003938	1.000454	0.971862	0.961682	0.893871	0.884509	0.000000	vaccine	All	ratio (total_dose/pop.)	0.817503
8	BG	Bulgaria	Malignant neoplasms (C00-C97)	normalized	1.025194	0.983432	0.991373	0.992287	1.036849	1.051645	0.977034	0.925674	0.000000	vaccine	All	ratio (total_dose/pop.)	0.666504
11	CH	Switzerland	Malignant neoplasms (C00-C97)	normalized	1.001197	0.997778	1.001026	1.005698	0.997493	0.977379	0.973789	0.995840	0.000000	nan	nan	nan	nan
83	RS	Serbia	Malignant neoplasms (C00-C97)	normalized	0.996101	1.003138	1.000761	1.006913	0.994470	0.967767	0.931046	0.901734	0.000000	nan	nan	nan	nan

Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)

sorted by 2022 column

	abbr	name	filter_x	value_type_x	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000	source	filter_y	value_type_y	value
12	CY	Cyprus	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.933333	1.133333	0.933333	0.533333	1.666667	1.066667	1.400000	1.466667	0.000000	vaccine	All	ratio (total_dose/pop.)	1.988003
45	IE	Ireland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.049080	0.926380	1.024540	1.104294	1.233129	1.141104	1.098160	1.190184	0.000000	vaccine	All	ratio (total_dose/pop.)	2.405531
18	DE	Germany	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.925963	1.018763	1.055274	1.085700	1.061359	1.032454	1.081643	1.152637	0.000000	vaccine	All	ratio (total_dose/pop.)	2.211555
87	SI	Slovenia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.728972	1.205607	1.065421	1.261682	1.345794	1.065421	1.065421	1.149533	0.000000	vaccine	All	ratio (total_dose/pop.)	1.379628
3	BE	Belgium	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.947814	1.095910	0.956276	1.315938	1.121298	1.002821	0.875882	1.108604	0.000000	vaccine	All	ratio (total_dose/pop.)	2.411964
75	PT	Portugal	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.049734	0.953819	0.996448	1.246892	1.278863	1.087034	1.134991	1.076377	0.000000	vaccine	All	ratio (total_dose/pop.)	5.294853
33	FI	Finland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.886239	1.078899	1.034862	0.963303	0.935780	0.941284	1.056881	1.073394	0.000000	vaccine	All	ratio (total_dose/pop.)	6.993552
30	ES	Spain	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.977254	1.030869	0.991877	1.027214	0.993095	1.002843	1.015028	1.050366	1.012591	vaccine	All	ratio (total_dose/pop.)	2.196480
9	CH	Switzerland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.030508	1.010169	0.959322	0.847458	0.888136	0.830508	0.820339	1.040678	0.000000	nan	nan	nan	nan
84	SE	Sweden	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.807259	1.073842	1.118899	1.122653	0.987484	1.025031	0.976220	1.040050	0.000000	vaccine	All	ratio (total_dose/pop.)	4.336310
0	AT	Austria	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.008816	1.031486	0.959698	1.027708	1.005038	1.008816	0.944584	1.039043	1.031486	vaccine	All	ratio (total_dose/pop.)	2.141405
66	NL	Netherlands	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.968590	0.975164	1.056245	1.130752	1.130752	0.981738	0.966399	1.010226	0.000000	vaccine	All	ratio (total_dose/pop.)	2.195485
36	FR	France	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.017804	1.008309	0.973887	0.900297	0.960237	0.897923	0.900890	0.999407	0.000000	vaccine	All	ratio (total_dose/pop.)	4.584656
15	CZ	Czechia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.977597	1.026477	0.995927	1.014257	0.916497	0.940937	0.910387	0.983707	0.837067	vaccine	All	ratio (total_dose/pop.)	1.685242
51	IT	Italy	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.039006	0.918261	1.042733	0.980870	0.948820	1.005466	0.983106	0.975652	0.000000	vaccine	All	ratio (total_dose/pop.)	4.765223
21	DK	Denmark	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.946988	1.026506	1.026506	1.091566	0.932530	1.055422	0.881928	0.939759	0.000000	vaccine	All	ratio (total_dose/pop.)	2.402740
72	PL	Poland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.981561	0.970969	1.047470	1.043939	0.927423	0.915653	0.993331	0.939192	0.000000	vaccine	All	ratio (total_dose/pop.)	2.862610
63	MT	Malta	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.913043	0.847826	1.239130	1.173913	1.239130	1.173913	1.173913	0.913043	0.000000	vaccine	All	ratio (total_dose/pop.)	2.322112
48	IS	Iceland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.900000	1.050000	1.050000	0.450000	1.500000	1.950000	0.900000	0.900000	0.750000	vaccine	All	ratio (total_dose/pop.)	2.138589
81	RS	Serbia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.918750	1.050000	1.031250	1.031250	1.068750	0.984375	0.900000	0.890625	0.000000	nan	nan	nan	nan
90	SK	Slovakia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.042316	1.042316	0.915367	1.189310	1.155902	0.968820	0.855234	0.888641	0.761693	vaccine	All	ratio (total_dose/pop.)	1.279721
27	EL	Greece	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.862559	1.127962	1.009479	0.872038	0.928910	1.071090	0.848341	0.886256	0.000000	vaccine	All	ratio (total_dose/pop.)	2.032484
42	HU	Hungary	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.025612	0.978842	0.995546	0.988864	0.881960	0.895323	0.841871	0.865256	0.000000	vaccine	All	ratio (total_dose/pop.)	1.617727
69	NO	Norway	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.916031	1.045802	1.038168	0.916031	0.977099	0.969466	1.022901	0.832061	0.000000	vaccine	All	ratio (total_dose/pop.)	2.212603
78	RO	Romania	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.047894	0.988439	0.963666	0.886870	0.775392	0.837325	0.797688	0.815029	0.000000	vaccine	All	ratio (total_dose/pop.)	0.817503
39	HR	Croatia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.088328	1.003155	0.908517	0.899054	0.690852	0.917981	0.908517	0.804416	0.000000	vaccine	All	ratio (total_dose/pop.)	1.390181
24	EE	Estonia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.759494	0.949367	1.291139	1.518987	0.797468	0.797468	0.835443	0.797468	0.000000	vaccine	All	ratio (total_dose/pop.)	1.582880
57	LU	Luxembourg	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.870968	1.258065	0.870968	1.064516	0.677419	0.870968	1.258065	0.774194	1.161290	vaccine	All	ratio (total_dose/pop.)	1.914499
54	LT	Lithuania	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.098214	1.017857	0.883929	0.937500	0.723214	0.736607	0.790179	0.696429	0.000000	vaccine	All	ratio (total_dose/pop.)	3.106103
6	BG	Bulgaria	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.184416	0.989610	0.825974	0.763636	0.864935	0.732468	0.740260	0.592208	0.000000	vaccine	All	ratio (total_dose/pop.)	0.666504
60	LV	Latvia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.006452	1.122581	0.870968	0.851613	0.677419	0.735484	0.716129	0.561290	0.000000	vaccine	All	ratio (total_dose/pop.)	1.503200

sorted by value (ratio (total_dose/pop.))

	abbr	name	filter_x	value_type_x	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000	source	filter_y	value_type_y	value
33	FI	Finland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.886239	1.078899	1.034862	0.963303	0.935780	0.941284	1.056881	1.073394	0.000000	vaccine	All	ratio (total_dose/pop.)	6.993552
75	PT	Portugal	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.049734	0.953819	0.996448	1.246892	1.278863	1.087034	1.134991	1.076377	0.000000	vaccine	All	ratio (total_dose/pop.)	5.294853
51	IT	Italy	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.039006	0.918261	1.042733	0.980870	0.948820	1.005466	0.983106	0.975652	0.000000	vaccine	All	ratio (total_dose/pop.)	4.765223
36	FR	France	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.017804	1.008309	0.973887	0.900297	0.960237	0.897923	0.900890	0.999407	0.000000	vaccine	All	ratio (total_dose/pop.)	4.584656
84	SE	Sweden	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.807259	1.073842	1.118899	1.122653	0.987484	1.025031	0.976220	1.040050	0.000000	vaccine	All	ratio (total_dose/pop.)	4.336310
54	LT	Lithuania	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.098214	1.017857	0.883929	0.937500	0.723214	0.736607	0.790179	0.696429	0.000000	vaccine	All	ratio (total_dose/pop.)	3.106103
72	PL	Poland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.981561	0.970969	1.047470	1.043939	0.927423	0.915653	0.993331	0.939192	0.000000	vaccine	All	ratio (total_dose/pop.)	2.862610
3	BE	Belgium	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.947814	1.095910	0.956276	1.315938	1.121298	1.002821	0.875882	1.108604	0.000000	vaccine	All	ratio (total_dose/pop.)	2.411964
45	IE	Ireland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.049080	0.926380	1.024540	1.104294	1.233129	1.141104	1.098160	1.190184	0.000000	vaccine	All	ratio (total_dose/pop.)	2.405531
21	DK	Denmark	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.946988	1.026506	1.026506	1.091566	0.932530	1.055422	0.881928	0.939759	0.000000	vaccine	All	ratio (total_dose/pop.)	2.402740
63	MT	Malta	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.913043	0.847826	1.239130	1.173913	1.239130	1.173913	1.173913	0.913043	0.000000	vaccine	All	ratio (total_dose/pop.)	2.322112
69	NO	Norway	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.916031	1.045802	1.038168	0.916031	0.977099	0.969466	1.022901	0.832061	0.000000	vaccine	All	ratio (total_dose/pop.)	2.212603
18	DE	Germany	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.925963	1.018763	1.055274	1.085700	1.061359	1.032454	1.081643	1.152637	0.000000	vaccine	All	ratio (total_dose/pop.)	2.211555
30	ES	Spain	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.977254	1.030869	0.991877	1.027214	0.993095	1.002843	1.015028	1.050366	1.012591	vaccine	All	ratio (total_dose/pop.)	2.196480
66	NL	Netherlands	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.968590	0.975164	1.056245	1.130752	1.130752	0.981738	0.966399	1.010226	0.000000	vaccine	All	ratio (total_dose/pop.)	2.195485
0	AT	Austria	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.008816	1.031486	0.959698	1.027708	1.005038	1.008816	0.944584	1.039043	1.031486	vaccine	All	ratio (total_dose/pop.)	2.141405
48	IS	Iceland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.900000	1.050000	1.050000	0.450000	1.500000	1.950000	0.900000	0.900000	0.750000	vaccine	All	ratio (total_dose/pop.)	2.138589
27	EL	Greece	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.862559	1.127962	1.009479	0.872038	0.928910	1.071090	0.848341	0.886256	0.000000	vaccine	All	ratio (total_dose/pop.)	2.032484
12	CY	Cyprus	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.933333	1.133333	0.933333	0.533333	1.666667	1.066667	1.400000	1.466667	0.000000	vaccine	All	ratio (total_dose/pop.)	1.988003
57	LU	Luxembourg	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.870968	1.258065	0.870968	1.064516	0.677419	0.870968	1.258065	0.774194	1.161290	vaccine	All	ratio (total_dose/pop.)	1.914499
15	CZ	Czechia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.977597	1.026477	0.995927	1.014257	0.916497	0.940937	0.910387	0.983707	0.837067	vaccine	All	ratio (total_dose/pop.)	1.685242
42	HU	Hungary	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.025612	0.978842	0.995546	0.988864	0.881960	0.895323	0.841871	0.865256	0.000000	vaccine	All	ratio (total_dose/pop.)	1.617727
24	EE	Estonia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.759494	0.949367	1.291139	1.518987	0.797468	0.797468	0.835443	0.797468	0.000000	vaccine	All	ratio (total_dose/pop.)	1.582880
60	LV	Latvia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.006452	1.122581	0.870968	0.851613	0.677419	0.735484	0.716129	0.561290	0.000000	vaccine	All	ratio (total_dose/pop.)	1.503200
39	HR	Croatia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.088328	1.003155	0.908517	0.899054	0.690852	0.917981	0.908517	0.804416	0.000000	vaccine	All	ratio (total_dose/pop.)	1.390181
87	SI	Slovenia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.728972	1.205607	1.065421	1.261682	1.345794	1.065421	1.065421	1.149533	0.000000	vaccine	All	ratio (total_dose/pop.)	1.379628
90	SK	Slovakia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.042316	1.042316	0.915367	1.189310	1.155902	0.968820	0.855234	0.888641	0.761693	vaccine	All	ratio (total_dose/pop.)	1.279721
78	RO	Romania	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.047894	0.988439	0.963666	0.886870	0.775392	0.837325	0.797688	0.815029	0.000000	vaccine	All	ratio (total_dose/pop.)	0.817503
6	BG	Bulgaria	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.184416	0.989610	0.825974	0.763636	0.864935	0.732468	0.740260	0.592208	0.000000	vaccine	All	ratio (total_dose/pop.)	0.666504
9	CH	Switzerland	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	1.030508	1.010169	0.959322	0.847458	0.888136	0.830508	0.820339	1.040678	0.000000	nan	nan	nan	nan
81	RS	Serbia	Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)	normalized	0.918750	1.050000	1.031250	1.031250	1.068750	0.984375	0.900000	0.890625	0.000000	nan	nan	nan	nan

Diseases of the respiratory system (J00-J99)

sorted by 2022 column

	abbr	name	filter_x	value_type_x	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000	source	filter_y	value_type_y	value
91	SK	Slovakia	Diseases of the respiratory system (J00-J99)	normalized	1.021516	0.890940	1.087544	1.026846	0.966147	1.181405	2.025563	1.594157	1.340111	vaccine	All	ratio (total_dose/pop.)	1.279721
64	MT	Malta	Diseases of the respiratory system (J00-J99)	normalized	0.986072	0.941504	1.072423	1.089136	1.284123	1.200557	1.253482	1.456825	0.000000	vaccine	All	ratio (total_dose/pop.)	2.322112
79	RO	Romania	Diseases of the respiratory system (J00-J99)	normalized	0.997274	0.968615	1.034111	1.138241	1.181262	1.426691	1.592194	1.340648	0.000000	vaccine	All	ratio (total_dose/pop.)	0.817503
7	BG	Bulgaria	Diseases of the respiratory system (J00-J99)	normalized	0.929681	1.018976	1.051343	1.120897	0.965261	1.379141	1.567144	1.282730	0.000000	vaccine	All	ratio (total_dose/pop.)	0.666504
82	RS	Serbia	Diseases of the respiratory system (J00-J99)	normalized	1.055686	0.925837	1.018478	0.996646	1.044865	1.274758	1.366260	1.248560	0.000000	nan	nan	nan	nan
49	IS	Iceland	Diseases of the respiratory system (J00-J99)	normalized	0.836735	0.948980	1.214286	1.163265	0.857143	0.923469	0.933673	1.229592	1.040816	vaccine	All	ratio (total_dose/pop.)	2.138589
73	PL	Poland	Diseases of the respiratory system (J00-J99)	normalized	0.989726	0.938055	1.072220	1.123850	1.109924	1.171204	1.147222	1.226214	0.000000	vaccine	All	ratio (total_dose/pop.)	2.862610
25	EE	Estonia	Diseases of the respiratory system (J00-J99)	normalized	0.980036	1.007260	1.012704	1.194192	1.088929	0.932849	1.121597	1.136116	0.000000	vaccine	All	ratio (total_dose/pop.)	1.582880
16	CZ	Czechia	Diseases of the respiratory system (J00-J99)	normalized	0.988986	0.945151	1.065862	1.101009	1.085739	1.097718	1.027424	1.127731	1.140632	vaccine	All	ratio (total_dose/pop.)	1.685242
61	LV	Latvia	Diseases of the respiratory system (J00-J99)	normalized	0.898739	1.037395	1.063866	1.162185	0.984454	0.901261	0.983193	1.123109	0.000000	vaccine	All	ratio (total_dose/pop.)	1.503200
13	CY	Cyprus	Diseases of the respiratory system (J00-J99)	normalized	1.003663	0.906593	1.089744	0.974359	1.203297	1.139194	0.954212	1.082418	0.000000	vaccine	All	ratio (total_dose/pop.)	1.988003
37	FR	France	Diseases of the respiratory system (J00-J99)	normalized	0.995234	0.962637	1.042129	1.050499	1.059961	0.901281	0.846249	1.049755	0.000000	vaccine	All	ratio (total_dose/pop.)	4.584656
22	DK	Denmark	Diseases of the respiratory system (J00-J99)	normalized	0.965601	0.985910	1.048489	1.118829	1.008366	0.934724	0.987726	1.038417	0.000000	vaccine	All	ratio (total_dose/pop.)	2.402740
67	NL	Netherlands	Diseases of the respiratory system (J00-J99)	normalized	1.010924	0.955243	1.033833	1.120297	1.006788	0.837121	0.807451	1.033515	0.000000	vaccine	All	ratio (total_dose/pop.)	2.195485
1	AT	Austria	Diseases of the respiratory system (J00-J99)	normalized	0.950587	0.923174	1.126239	1.178745	1.127504	1.029873	0.852534	1.027553	1.148380	vaccine	All	ratio (total_dose/pop.)	2.141405
52	IT	Italy	Diseases of the respiratory system (J00-J99)	normalized	0.980644	0.940604	1.078753	1.046090	1.084513	1.154365	0.914167	1.024463	0.000000	vaccine	All	ratio (total_dose/pop.)	4.765223
10	CH	Switzerland	Diseases of the respiratory system (J00-J99)	normalized	1.036089	0.920944	1.042967	1.036311	1.022112	0.841739	0.808238	1.010353	0.000000	nan	nan	nan	nan
19	DE	Germany	Diseases of the respiratory system (J00-J99)	normalized	1.018797	0.960947	1.020256	1.069720	1.000015	0.914894	0.854677	1.008996	0.000000	vaccine	All	ratio (total_dose/pop.)	2.211555
70	NO	Norway	Diseases of the respiratory system (J00-J99)	normalized	0.959046	0.991522	1.049432	1.019001	0.995155	0.855715	0.828236	0.999016	0.000000	vaccine	All	ratio (total_dose/pop.)	2.212603
4	BE	Belgium	Diseases of the respiratory system (J00-J99)	normalized	1.008991	0.955891	1.035118	1.108343	1.041629	0.884865	0.778579	0.988275	0.000000	vaccine	All	ratio (total_dose/pop.)	2.411964
46	IE	Ireland	Diseases of the respiratory system (J00-J99)	normalized	0.977738	0.995446	1.026815	1.024791	0.994182	0.827473	0.810018	0.980015	0.000000	vaccine	All	ratio (total_dose/pop.)	2.405531
58	LU	Luxembourg	Diseases of the respiratory system (J00-J99)	normalized	1.036364	0.956150	1.007487	1.244920	1.241711	1.039572	0.959358	0.972193	1.273797	vaccine	All	ratio (total_dose/pop.)	1.914499
85	SE	Sweden	Diseases of the respiratory system (J00-J99)	normalized	0.989235	0.969670	1.041095	1.083795	0.959267	0.862378	0.788624	0.962838	0.000000	vaccine	All	ratio (total_dose/pop.)	4.336310
40	HR	Croatia	Diseases of the respiratory system (J00-J99)	normalized	0.968126	0.890546	1.141328	0.857349	0.841833	0.807914	0.910031	0.961270	0.000000	vaccine	All	ratio (total_dose/pop.)	1.390181
34	FI	Finland	Diseases of the respiratory system (J00-J99)	normalized	0.944544	1.039681	1.015775	1.088958	0.961132	0.854285	0.860628	0.946495	0.000000	vaccine	All	ratio (total_dose/pop.)	6.993552
28	EL	Greece	Diseases of the respiratory system (J00-J99)	normalized	1.047398	0.974959	0.977642	0.894401	0.959568	0.887906	0.923419	0.929491	0.000000	vaccine	All	ratio (total_dose/pop.)	2.032484
76	PT	Portugal	Diseases of the respiratory system (J00-J99)	normalized	1.016271	1.016573	0.967155	1.003823	0.923698	0.849835	0.774992	0.916681	0.000000	vaccine	All	ratio (total_dose/pop.)	5.294853
55	LT	Lithuania	Diseases of the respiratory system (J00-J99)	normalized	1.020121	0.968813	1.011066	1.044266	0.906942	0.908451	0.802062	0.876761	0.000000	vaccine	All	ratio (total_dose/pop.)	3.106103
43	HU	Hungary	Diseases of the respiratory system (J00-J99)	normalized	1.069291	0.906061	1.024648	1.020246	1.045649	0.900528	0.847208	0.864185	0.000000	vaccine	All	ratio (total_dose/pop.)	1.617727
31	ES	Spain	Diseases of the respiratory system (J00-J99)	normalized	1.035062	0.934527	1.030411	1.071775	0.951875	0.846907	0.709559	0.858905	0.938340	vaccine	All	ratio (total_dose/pop.)	2.196480
88	SI	Slovenia	Diseases of the respiratory system (J00-J99)	normalized	1.018009	0.986365	0.995626	0.835863	0.833548	0.622074	0.520196	0.540262	0.000000	vaccine	All	ratio (total_dose/pop.)	1.379628

sorted by value (ratio (total_dose/pop.))

	abbr	name	filter_x	value_type_x	2015.000000	2016.000000	2017.000000	2018.000000	2019.000000	2020.000000	2021.000000	2022.000000	2023.000000	source	filter_y	value_type_y	value
34	FI	Finland	Diseases of the respiratory system (J00-J99)	normalized	0.944544	1.039681	1.015775	1.088958	0.961132	0.854285	0.860628	0.946495	0.000000	vaccine	All	ratio (total_dose/pop.)	6.993552
76	PT	Portugal	Diseases of the respiratory system (J00-J99)	normalized	1.016271	1.016573	0.967155	1.003823	0.923698	0.849835	0.774992	0.916681	0.000000	vaccine	All	ratio (total_dose/pop.)	5.294853
52	IT	Italy	Diseases of the respiratory system (J00-J99)	normalized	0.980644	0.940604	1.078753	1.046090	1.084513	1.154365	0.914167	1.024463	0.000000	vaccine	All	ratio (total_dose/pop.)	4.765223
37	FR	France	Diseases of the respiratory system (J00-J99)	normalized	0.995234	0.962637	1.042129	1.050499	1.059961	0.901281	0.846249	1.049755	0.000000	vaccine	All	ratio (total_dose/pop.)	4.584656
85	SE	Sweden	Diseases of the respiratory system (J00-J99)	normalized	0.989235	0.969670	1.041095	1.083795	0.959267	0.862378	0.788624	0.962838	0.000000	vaccine	All	ratio (total_dose/pop.)	4.336310
55	LT	Lithuania	Diseases of the respiratory system (J00-J99)	normalized	1.020121	0.968813	1.011066	1.044266	0.906942	0.908451	0.802062	0.876761	0.000000	vaccine	All	ratio (total_dose/pop.)	3.106103
73	PL	Poland	Diseases of the respiratory system (J00-J99)	normalized	0.989726	0.938055	1.072220	1.123850	1.109924	1.171204	1.147222	1.226214	0.000000	vaccine	All	ratio (total_dose/pop.)	2.862610
4	BE	Belgium	Diseases of the respiratory system (J00-J99)	normalized	1.008991	0.955891	1.035118	1.108343	1.041629	0.884865	0.778579	0.988275	0.000000	vaccine	All	ratio (total_dose/pop.)	2.411964
46	IE	Ireland	Diseases of the respiratory system (J00-J99)	normalized	0.977738	0.995446	1.026815	1.024791	0.994182	0.827473	0.810018	0.980015	0.000000	vaccine	All	ratio (total_dose/pop.)	2.405531
22	DK	Denmark	Diseases of the respiratory system (J00-J99)	normalized	0.965601	0.985910	1.048489	1.118829	1.008366	0.934724	0.987726	1.038417	0.000000	vaccine	All	ratio (total_dose/pop.)	2.402740
64	MT	Malta	Diseases of the respiratory system (J00-J99)	normalized	0.986072	0.941504	1.072423	1.089136	1.284123	1.200557	1.253482	1.456825	0.000000	vaccine	All	ratio (total_dose/pop.)	2.322112
70	NO	Norway	Diseases of the respiratory system (J00-J99)	normalized	0.959046	0.991522	1.049432	1.019001	0.995155	0.855715	0.828236	0.999016	0.000000	vaccine	All	ratio (total_dose/pop.)	2.212603
19	DE	Germany	Diseases of the respiratory system (J00-J99)	normalized	1.018797	0.960947	1.020256	1.069720	1.000015	0.914894	0.854677	1.008996	0.000000	vaccine	All	ratio (total_dose/pop.)	2.211555
31	ES	Spain	Diseases of the respiratory system (J00-J99)	normalized	1.035062	0.934527	1.030411	1.071775	0.951875	0.846907	0.709559	0.858905	0.938340	vaccine	All	ratio (total_dose/pop.)	2.196480
67	NL	Netherlands	Diseases of the respiratory system (J00-J99)	normalized	1.010924	0.955243	1.033833	1.120297	1.006788	0.837121	0.807451	1.033515	0.000000	vaccine	All	ratio (total_dose/pop.)	2.195485
1	AT	Austria	Diseases of the respiratory system (J00-J99)	normalized	0.950587	0.923174	1.126239	1.178745	1.127504	1.029873	0.852534	1.027553	1.148380	vaccine	All	ratio (total_dose/pop.)	2.141405
49	IS	Iceland	Diseases of the respiratory system (J00-J99)	normalized	0.836735	0.948980	1.214286	1.163265	0.857143	0.923469	0.933673	1.229592	1.040816	vaccine	All	ratio (total_dose/pop.)	2.138589
28	EL	Greece	Diseases of the respiratory system (J00-J99)	normalized	1.047398	0.974959	0.977642	0.894401	0.959568	0.887906	0.923419	0.929491	0.000000	vaccine	All	ratio (total_dose/pop.)	2.032484
13	CY	Cyprus	Diseases of the respiratory system (J00-J99)	normalized	1.003663	0.906593	1.089744	0.974359	1.203297	1.139194	0.954212	1.082418	0.000000	vaccine	All	ratio (total_dose/pop.)	1.988003
58	LU	Luxembourg	Diseases of the respiratory system (J00-J99)	normalized	1.036364	0.956150	1.007487	1.244920	1.241711	1.039572	0.959358	0.972193	1.273797	vaccine	All	ratio (total_dose/pop.)	1.914499
16	CZ	Czechia	Diseases of the respiratory system (J00-J99)	normalized	0.988986	0.945151	1.065862	1.101009	1.085739	1.097718	1.027424	1.127731	1.140632	vaccine	All	ratio (total_dose/pop.)	1.685242
43	HU	Hungary	Diseases of the respiratory system (J00-J99)	normalized	1.069291	0.906061	1.024648	1.020246	1.045649	0.900528	0.847208	0.864185	0.000000	vaccine	All	ratio (total_dose/pop.)	1.617727
25	EE	Estonia	Diseases of the respiratory system (J00-J99)	normalized	0.980036	1.007260	1.012704	1.194192	1.088929	0.932849	1.121597	1.136116	0.000000	vaccine	All	ratio (total_dose/pop.)	1.582880
61	LV	Latvia	Diseases of the respiratory system (J00-J99)	normalized	0.898739	1.037395	1.063866	1.162185	0.984454	0.901261	0.983193	1.123109	0.000000	vaccine	All	ratio (total_dose/pop.)	1.503200
40	HR	Croatia	Diseases of the respiratory system (J00-J99)	normalized	0.968126	0.890546	1.141328	0.857349	0.841833	0.807914	0.910031	0.961270	0.000000	vaccine	All	ratio (total_dose/pop.)	1.390181
88	SI	Slovenia	Diseases of the respiratory system (J00-J99)	normalized	1.018009	0.986365	0.995626	0.835863	0.833548	0.622074	0.520196	0.540262	0.000000	vaccine	All	ratio (total_dose/pop.)	1.379628
91	SK	Slovakia	Diseases of the respiratory system (J00-J99)	normalized	1.021516	0.890940	1.087544	1.026846	0.966147	1.181405	2.025563	1.594157	1.340111	vaccine	All	ratio (total_dose/pop.)	1.279721
79	RO	Romania	Diseases of the respiratory system (J00-J99)	normalized	0.997274	0.968615	1.034111	1.138241	1.181262	1.426691	1.592194	1.340648	0.000000	vaccine	All	ratio (total_dose/pop.)	0.817503
7	BG	Bulgaria	Diseases of the respiratory system (J00-J99)	normalized	0.929681	1.018976	1.051343	1.120897	0.965261	1.379141	1.567144	1.282730	0.000000	vaccine	All	ratio (total_dose/pop.)	0.666504
82	RS	Serbia	Diseases of the respiratory system (J00-J99)	normalized	1.055686	0.925837	1.018478	0.996646	1.044865	1.274758	1.366260	1.248560	0.000000	nan	nan	nan	nan
10	CH	Switzerland	Diseases of the respiratory system (J00-J99)	normalized	1.036089	0.920944	1.042967	1.036311	1.022112	0.841739	0.808238	1.010353	0.000000	nan	nan	nan	nan

notable CODs x Vaccines

lets put the CODs on an XY axis with the Vaccine values.

# codxv
temp = codxv[codxv.value < 14].copy()
temp = temp.dropna(subset=['value'])
# display(temp)


codlist = ['Malignant neoplasms (C00-C97)','Congenital malformations, deformations and chromosomal abnormalities (Q00-Q99)','Diseases of the respiratory system (J00-J99)']
for c in codlist:

    # kde 
    g = sns.jointplot(
        data=temp[temp['filter_x'] == c],
        x="value", 
        y=2022, 
        kind="kde",
        fill=True,
        cmap=heatmapCM
    )

    # Add scatter points on top
    g.ax_joint.scatter(
        temp[temp['filter_x'] == c]["value"], 
        temp[temp['filter_x'] == c][2022], 
        color="black", 
        alpha=0.7, 
        s=7
    )

    min_c = c
    if len(min_c) >= 50:
        min_c = min_c[0:25] + '...' + min_c[-10::]
    

    g.set_axis_labels( "Vaccines (ratio (total_dose/pop.))",min_c)

    g.figure.suptitle(f"Kde Plot : {min_c} x Vaccines", fontsize=14)

    # Add a title
    # plt.title("<-- hypothetical 0 vaccines", fontsize=10,x=0.34,y=0.25)

    # Add vertical and horizontal lines
    plt.axvline(x=0.0, color='black', linestyle='--', linewidth=1)  # Vertical line at x=20
    plt.axhline(y=1.0, color='black', linestyle='--', linewidth=1)  # Horizontal line at y=3

    # display(vdravg)
    plt.show()

Post Axis Notes:

• Compare the Charts...
  • Kde Plot : Death x Vaccines
    • Y: Average Normalized Deaths between 2022-2024
    • X: Vaccines (ratio (total_dose/pop.))
  • Kde Plot : Malignant neoplasms (C00-C97) x Vaccines
    • Y: Normalized Malignant neoplasms (aka Cancer) Deaths (in 2022)
    • X: Vaccines (ratio (total_dose/pop.))

here is the Scatter Chart of Malignant neoplasms (C00-C97) x Vaccine

compare it with

title = 'Scatter Chart - Malignant neoplasms (C00-C97) x Vaccine'
display(MD(f'### {title}'))


temp = codxv[codxv.filter_x == 'Malignant neoplasms (C00-C97)']

fig = px.scatter(
        temp,
        x="value",      
        y=2022,       
        color="name",      
        hover_name="name",
        title=title,
        labels={
            "value": "Vaccines (ratio total_dose/pop.)",  
            "Malignant neoplasms (C00-C97) 2022": 2022          
        },
        height=750
    )
fig.update_layout(template="plotly_dark")

fig.add_shape(
    type="line",
    x0=0, x1=dxv.value.max(),
    y0=1, y1=1, # Start and end y-coordinates
    line=dict(color="Red", width=2, dash="dash"),  # Line style
    xref="x", yref="y"          # Reference axes
)


fig.show()

Scatter Chart - Malignant neoplasms (C00-C97) x Vaccine

misc_and_style

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Excess Mortality And Vaccines In Europe (v2) #Table of Contents #Prerequisites ##Scientific Method ##Logical Fallacies ###About Appeal to Authority ####And More ... ###About Post Hoc Ergo Propter Hoc (Correlation vs Causation) #Observation ##News Articles & Headlines ###Ad Hominem Attacks ###Appeals to Authority ###Appeal to Emotions ##Denial of Aid #Hypothesis #below we will be #Setup #Helping Functions #Import and Clean Data ##death data ###Getting the Data ###variables ##cause of death ###Getting the Data ###variables ##vaccine data ###Getting the Data ###variables #combine all data ##variables #Lets Visualize the Data ##Line Charts - normalized deaths ##HeatMaps of deaths by AgeGroups ###Post HeatMaps Notes ##Spearman and Kendall ##Spearman and Kendall ... continued. ##Other death x vaccine charts ##lets look at Deaths for each year 2015-2024 separately ##notes: ##Bad-Batch Theory ###There seems to be to be an unusual grouping/cluster ###Bad-Batch Theory (by Vibeke Manniche) ###my 2 cents #Lets look at some of the Causes of Death ##Hypothesizing Adverse Reactions from an mRNA Vaccine ###Spearman and Kendall ... (again) ###Note: #Lets see the Spearmans Rank over time ##Note: #lets see these overtime #notable CODs x Vaccines ##Post Axis Notes: ##here is the Scatter Chart of Malignant neoplasms (C00-C97) x Vaccine misc_and_style sidelinks { sidelinks a { sidelinks a:hover {

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